@article {28485,
	title = {The connectivity network underlying the German{\textquoteright}s Twittersphere: a testbed for investigating information spreading phenomena},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {Jan-12-2022},
	publisher = {Nature Publishing Group},
	abstract = {Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose {\textquotedblleft}friend{\textquotedblright} or {\textquotedblleft}follower{\textquotedblright} edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance {\textquotedblleft}strengths{\textquotedblright} underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet{\textendash}retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.},
	doi = {10.1038/s41598-022-07961-3},
	url = {https://www.nature.com/articles/s41598-022-07961-3},
	author = {Daniel Thilo {Schroeder} and Langguth, Johannes and Burchard, Luk and Pogorelov, Konstantin and Pedro G. {Lind}}
}
@article {42552,
	title = {COVID-19 and 5G conspiracy theories: Long term observation of a digital wildfire},
	journal = {International Journal of Data Science and Analytics },
	year = {2022},
	publisher = { Springer},
	author = {Langguth, Johannes and Filkukova, Petra and Brenner, Stefan and Daniel Thilo {Schroeder} and Pogorelov, Konstantin}
}
@inproceedings {42953,
	title = {Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs},
	journal = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	pages = {45-54},
	publisher = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00016},
	url = {https://ieeexplore.ieee.org/document/9835385/http://xplorestaging.ieee.org/ielx7/9835152/9835156/09835385.pdf?arnumber=9835385},
	author = {Moe, Johannes and Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Langguth, Johannes}
}
@inproceedings {42693,
	title = {A Streaming System for Large-scale Temporal Graph Mining of Reddit Data},
	journal = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	pages = {1153-1162},
	publisher = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00189},
	url = {https://ieeexplore.ieee.org/document/9835250/http://xplorestaging.ieee.org/ielx7/9835152/9835156/09835250.pdf?arnumber=9835250},
	author = {Huber, Andreas and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Griwodz, Carsten and Langguth, Johannes}
}
@article {27774,
	title = {A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging},
	journal = {Medical Image Analysis},
	volume = {70},
	year = {2021},
	pages = {102007},
	publisher = {Elsevier},
	abstract = {Gastrointestinal endoscopy has\&nbsp;been\&nbsp;an\&nbsp;active field\&nbsp;of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. \&nbsp; The high missed rate of such abnormalities during endoscopy is thus a\&nbsp; critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic\&nbsp; GI\&nbsp; disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the\&nbsp; Medico\&nbsp; GI\&nbsp; challenges:\&nbsp; Medical\&nbsp; Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018,\&nbsp; and\&nbsp; BioMedia\&nbsp; ACM\&nbsp; MM\&nbsp; Grand\&nbsp; Challenge\&nbsp; 2019.\&nbsp; These challenges are initiative to set-up\&nbsp; a\&nbsp; benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a\&nbsp; period of three consecutive years and provide a\&nbsp; detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings.\&nbsp; Our analysis revealed that the participants achieved an improvement on maximum\&nbsp; Mathew correlation coefficient\&nbsp; (MCC)\&nbsp; from\&nbsp; 82.68\%\&nbsp; in\&nbsp; 2017\&nbsp; to\&nbsp; 93.98\%\&nbsp; in\&nbsp; 2018\&nbsp; and 95.20\% in 2019 challenges, and a significant increase in computational speed over consecutive years.},
	keywords = {Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018},
	doi = {https://doi.org/10.1016/j.media.2021.102007},
	author = {Jha, Debesh and Ali, Sharib and Hicks, Steven and Thambawita, Vajira and Borgli, Hanna and Pia H. {Smedsrud} and de Lange, Thomas and Pogorelov, Konstantin and Wang, Xiaowei and Harzig, Philipp and Tran, Minh-Triet and Meng, Wenhua and Hoang, Trung-Hieu and Dias, Danielle and Tobey H. {Ko} and Agrawal, Taruna and Ostroukhova, Olga and Khan, Zeshan and Muhammad Atif {Tahir} and Liu, Yang and Chang, Yuan and Kirker{\o}d, Mathias and Johansen, Dag and Lux, Mathias and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27739,
	title = {Don{\textquoteright}t Trust Your Eyes: Image Manipulation in the Age of DeepFakes},
	journal = {Frontiers in Communication},
	volume = {6},
	year = {2021},
	publisher = {Frontiers Media SA},
	address = {Lausanne, Switzerland },
	doi = {10.3389/fcomm.2021.632317},
	url = {https://www.frontiersin.org/articles/10.3389/fcomm.2021.632317/full},
	author = {Langguth, Johannes and Pogorelov, Konstantin and Brenner, Stefan and Filkukova, Petra and Daniel Thilo {Schroeder}}
}
@inproceedings {28400,
	title = {Explaining the Performance of Supervised and Semi-Supervised Methods for Automated Sparse Matrix Format Selection},
	journal = {50th International Conference on Parallel Processing Workshop},
	year = {2021},
	month = {08/2021},
	pages = {1-10},
	publisher = {ACM},
	address = {Chicago, Illinois, USA},
	author = {Dhandhania, Sunidhi and Deodhar, Akshay and Pogorelov, Konstantin and Biswas, Swarnendu and Langguth, Johannes}
}
@inproceedings {28037,
	title = {iPUG: Accelerating Breadth-First Graph~Traversals Using Manycore Graphcore~IPUs},
	journal = {High Performance Computing. ISC High Performance 2021},
	volume = {LNCS, volume 12728},
	year = {2021},
	pages = {291-309},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {The Graphcore Intelligence Processing Unit (IPU) is a newly developed processor type whose architecture does not rely on the traditional caching hierarchies. Developed to meet the need for more and more data-centric applications, such as machine learning, IPUs combine a dedicated portion of SRAM with each of its numerous cores, resulting in high memory bandwidth at the price of capacity. The proximity of processor cores and memory makes the IPU a promising field of experimentation for graph algorithms since it is the unpredictable, irregular memory accesses that lead to performance losses in traditional processors with pre-caching.This paper aims to test the IPU{\textquoteright}s suitability for algorithms with hard-to-predict memory accesses by implementing a breadth-first search (BFS) that complies with the Graph500 specifications. Precisely because of its apparent simplicity, BFS is an established benchmark that is not only subroutine for a variety of more complex graph algorithms, but also allows comparability across a wide range of architectures.We benchmark our IPU code on a wide range of instances and compare its performance to state-of-the-art CPU and GPU codes. The results indicate that the IPU delivers speedups of up\&nbsp;to\&nbsp;4{\texttimes}4{\texttimes}\&nbsp;over the fastest competing result on an NVIDIA V100 GPU, with typical speedups of about\&nbsp;1.5{\texttimes}1.5{\texttimes}\&nbsp;on most test instances.},
	keywords = {BFS, Graph500, IPU, Performance optimization},
	isbn = {978-3-030-78712-7},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-78713-4},
	url = {https://link.springer.com/10.1007/978-3-030-78713-4},
	author = {Burchard, Luk and Moe, Johannes and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes},
	editor = {Bradford L. {Chamberlain} and Varbanescu, Ana-Lucia and Ltaief, Hatem and Luszczek, Piotr}
}
@inproceedings {27562,
	title = {WICO Graph: a Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets},
	journal = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021)},
	volume = {2},
	year = {2021},
	pages = {257-266},
	publisher = {SCITEPRESS},
	abstract = {In the wake of the COVID-19 pandemic, a surge of misinformation has flooded social media and other internet channels, and some of it has the potential to cause real-world harm. To counteract this misinformation, reliably identifying it is a principal problem to be solved. However, the identification of misinformation poses a formidable challenge for language processing systems since the texts containing misinformation are short, work with insinuation rather than explicitly stating a false claim, or resemble other postings that deal with the same topic ironically. Accordingly, for the development of better detection systems, it is not only essential to use hand-labeled ground truth data and extend the analysis with methods beyond Natural Language Processing to consider the characteristics of the participant{\textquoteright}s relationships and the diffusion of misinformation. This paper presents a novel dataset that deals with a specific piece of misinformation: the idea that the 5G wireless network is causally connected to the COVID-19 pandemic. We have extracted the subgraphs of 3,000 manually classified Tweets from Twitter{\textquoteright}s follower network and distinguished them into three categories. First, subgraphs of Tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and Tweets that do neither. We created the WICO (Wireless Networks and Coronavirus Conspiracy) dataset to support experts in machine learning experts, graph processing, and related fields in studying the spread of misinformation. Furthermore, we provide a series of baseline experiments using both Graph Neural Networks and other established classifiers that use simple graph metrics as features. The dataset is available at https://datasets.simula.no/wico-graph\&nbsp;},
	isbn = {978-989-758-484-8},
	doi = {10.5220/0010262802570266},
	author = {Daniel Thilo {Schroeder} and Schaal, Ferdinand and Filkukova, Petra and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {28019,
	title = {WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets},
	journal = {Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks (OASIS {\textquoteright}21)},
	year = {2021},
	pages = {21-25},
	publisher = {ACM},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Filkukova, Petra and Brenner, Stefan and Langguth, Johannes}
}
@misc {27470,
	title = {Will technical means help in preventing digital wildfires?},
	howpublished = {6th World Conference on Media and Mass Communication, Cagliari, Italy},
	year = {2021},
	author = {Langguth, Johannes and Filkukova, Petra and Daniel Thilo {Schroeder} and Pogorelov, Konstantin}
}
@inproceedings {27671,
	title = {Evaluating Standard Classifiers for Detecting COVID-19 related Misinformation},
	journal = {MediaEval 2020},
	year = {2020},
	publisher = {CEUR},
	author = {Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {27672,
	title = {FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020},
	journal = {Media Eval Challange 2020},
	year = {2020},
	publisher = {CEUR},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Burchard, Luk and Moe, Johannes and Brenner, Stefan and Filkukova, Petra and Langguth, Johannes},
	editor = {Larson, Martha}
}
@misc {27673,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	year = {2020},
	address = {Complex Networks},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {27564,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	journal = {Complex Networks},
	year = {2020},
	note = {Extended abstract},
	publisher = {Springer},
	abstract = {Online social networks create a digital footprint of human interaction naturally by the way they function. Thus, they allow a large-scale analysis of human behavior which was previously infeasible for social scientists. Consequently, social networks have been studied intensely in the last decade. The core of most social networks is the relationship between users which can be described as a graph. The graph can be either undirected, as is the case for the friendship relation of Facebook, or directed, which is the case of the follower relation on Twitter. The relationship is readily visible, e.g. on the user interface of the social networks themselves. However, these edges are unweighted expressions of interest and reflect how individuals have chosen to relate to each other rather than how they actually interact with each other. For studying information propagation, comparing interaction properties is crucial and, therefore, using models based on connections that reflect different dimensions and strengths of acquaintance seems appropriate. Thus, there is a need for obtaining weighted edges from the communication that occurs on the social network. In this paper, we present a novel method to calculate an acquaintance score between pairs of Twitter users and use the resulting networks to enable the analysis of interaction based information propagation. By understanding the frequency and velocity with which individuals share content as a measure of acquaintance, it becomes possible to predict, compare communication patterns, and detect unusual communication. In contrast to previous work which assigns edge weights based on tie strength, our score considers the response time as a crucial factor and, therefore, enables time-based spreading comparisons.},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@misc {27395,
	title = {Graph Structure Based Monitoring of Digital Wildfires},
	year = {2020},
	month = {07/2020},
	type = {Poster},
	address = {6th International Conference on Computational Social Science, Boston, MA, USA},
	keywords = {Digital wildfires, Graph neural networks, Large scale infrastructure, Misinformation, Social network analysis},
	url = {http://2020.ic2s2.org/program},
	author = {Langguth, Johannes and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Filkukova, Petra}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The\&nbsp;HyperKvasir\&nbsp;dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {27561,
	title = {A Scalable System for Bundling Online Social Network Mining Research},
	journal = {2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)},
	year = {2020},
	pages = {1-6},
	publisher = {IEEE},
	abstract = {Online social networks such as Facebook and Twitter are part of the everyday life of millions of people. They are not only used for interaction but play an essential role when it comes to information acquisition and knowledge gain. The abundance and detail of the accumulated data in these online social networks open up new possibilities for social researchers and psychologists, allowing them to study behavior in a large test population. However, complex application programming interfaces (API) and data scraping restrictions are, in many cases, a limiting factor when accessing this data. Furthermore, research projects are typically granted restricted access based on quotas. Thus, research tools such as scrapers that access social network data through an API must manage these quotas. While this is generally feasible, it becomes a problem when more than one tool, or multiple instances of the same tool, is being used in the same research group. Since different tools typically cannot balance access to a shared quota on their own, additional software is needed to prevent the individual tools from overusing the shared quota. In this paper, we present a proxy server that manages several researchers{\textquoteright} data contingents in a cooperative research environment and thus enables a transparent view of a subset of Twitter{\textquoteright}s API. Our proxy scales linearly with the number of clients in use and incurs almost no performance penalties or implementation overhead to further layer or applications that need to work with the Twitter API. Thus, it allows seamless integration of multiple API accessing programs within the same research group.},
	author = {Burchard, Luk and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Becker, Soeren and Dietrich, Emily and Filkukova, Petra and Langguth, Johannes}
}
@inproceedings {27396,
	title = {A System for High Performance Mining on GDELT Data},
	journal = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2020},
	month = {05/2020},
	publisher = {IEEE},
	abstract = {We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system{\textquoteright}s ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today{\textquoteright}s online news.},
	keywords = {Data mining, GDELT, High Performance Computing, Misinformation, Publishing},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Filkukova, Petra and Langguth, Johannes}
}
@inproceedings {26858,
	title = {ACM Multimedia BioMedia 2019 Grand Challenge Overview},
	journal = {The ACM International Conference on Multimedia (ACM MM)},
	year = {2019},
	month = {10/2019},
	pages = {2563-2567},
	publisher = {ACM Press},
	address = {New York, New York, USA},
	abstract = {The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year{\textquoteright}s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year{\textquoteright}s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.},
	isbn = {9781450368896},
	doi = {10.1145/334303110.1145/3343031.3356058},
	url = {http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cfm?doid=3343031.3356058http://dl.acm.org/ft_gateway.cfm?id=3356058\&ftid=2092095\&dwn=1},
	author = {Hicks, Steven and Petlund, Andreas and de Lange, Thomas and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l and Riegler, Michael and Pia H. {Smedsrud} and Trine B. {Haugen} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Lux, Mathias},
	editor = {Larson, Martha and Gravier, Guillaume and Hung, Hayley and Ngo, Chong-Wah and Wei Tsang {Ooi}}
}
@article {26455,
	title = {Automatic detection of passable roads after floods in remote sensed and social media data},
	journal = {Signal Processing: Image Communication},
	volume = {74},
	year = {2019},
	pages = {110-118},
	publisher = {Elsevier},
	abstract = {This paper addresses the problem of floods classification and floods aftermath detection based onboth social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.},
	keywords = {convolutional neural networks, Flood detection, Multimedia Indexing and Retrieval, Natural Disasters, Satellite Imagery, Social Media},
	doi = {https://doi.org/10.1016/j.image.2019.02.002},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola and Dahyot, Rozenn}
}
@article {pogorelov2019bleeding,
	title = {Bleeding detection in wireless capsule endoscopy videos{\textemdash}Color versus texture features},
	journal = {Journal of applied clinical medical physics},
	volume = {20},
	year = {2019},
	pages = {141-154},
	publisher = {Wiley Online Library},
	author = {Pogorelov, Konstantin and Suman, Shipra and Fawnizu Azmadi {Hussin} and Aamir Saeed {Malik} and Ostroukhova, Olga and Riegler, Michael and Halvorsen, P{\r a}l and Shiaw Hooi {Ho} and Goh, Khean-Lee}
}
@article {26546,
	title = {Deep Learning for Automatic Generation of Endoscopy Reports},
	journal = {Gastrointestinal Endoscopy},
	volume = {89},
	year = {2019},
	month = {06/2019},
	publisher = {Elsevier},
	address = {Gastrointestinal Endoscopy},
	doi = {10.1016/j.gie.2019.04.053},
	author = {Hicks, Steven and Pia H. {Smedsrud} and Riegler, Michael and de Lange, Thomas and Petlund, Andreas and Sigrun Losada {Eskeland} and Pogorelov, Konstantin and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l}
}
@phdthesis {27220,
	title = {DeepEIR: A Holistic Medical Multimedia System for Gastrointestinal Tract Disease Detection and Localization},
	volume = {PhD},
	year = {2019},
	school = {Department of Informatics, University of Oslo},
	author = {Pogorelov, Konstantin}
}
@article {26631,
	title = {Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {International Journal of Semantic Computing},
	volume = {13},
	number = {3},
	year = {2019},
	pages = {367-391},
	publisher = {World Scientific},
	abstract = {360\&nbsp;panorama video displayed through Virtual reality (VR) glasses or large screens o{\textregistered}ers immersive user experiences, but as such technology becomes commonplace, the need for e{\textpm}cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di{\textregistered}erent trade-o{\textregistered}s in order to save bandwidth while preserving the video quality in the user{\textquoteright}s ̄eld-of-view (FoV). Using 360\&nbsp;VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e{\textpm}cient end-to-end design and real-world implementation of such a 360\&nbsp;streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a\&nbsp;live\&nbsp;streaming system which strikes a trade-o{\textregistered} between bandwidth usage and the video quality in the user{\textquoteright}s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o{\textregistered}s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Johansen, Dag and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Halvorsen, P{\r a}l}
}
@misc {runeGTC2019,
	title = {Efficient Processing of Medical Videos in a Multi-auditory Environment Using Gpu Lending},
	year = {2019},
	address = {NVIDIA{\textquoteright}s GPU Technology Conference (GTC)},
	author = {Borgli, Rune and Pogorelov, Konstantin and Riegler, Michael and Markussen, Jonas and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun Losadal {Eskeland} and de Lange, Thomas}
}
@inproceedings {26680,
	title = {FACT: a Framework for Analysis and Capture of Twitter Graphs},
	journal = {The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) },
	year = {2019},
	pages = {134-141},
	publisher = {IEEE},
	abstract = {In the recent years, online social networks have become an important source of news and the primary place for political debates for a growing part of the population. At the same time, the spread of fake news and digital wildfires (fast- spreading and harmful misinformation) has become a growing concern worldwide, and online social networks the problem is most prevalent. Thus, the study of social networks is an essential component in the understanding of the fake news phenomenon. Of particular interest is the network connectivity between participants, since it makes communication patterns visible. These patterns are hidden in the offline world, but they have a profound impact on the spread of ideas, opinions and news. Among the major social networks, Twitter is of special interest. Because of its public nature, Twitter offers the possibility to perform research without the risk of breaching the expectation of privacy. However, obtaining sufficient amounts of data from Twitter is a fundamental challenge for many researchers. Thus, in this paper, we present a scalable framework for gathering the graph structure of follower networks, posts and profiles. We also show how to use the collected data for high-performance social network analysis.\&nbsp;},
	doi = {10.1109/SNAMS.2019.8931870},
	author = {Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {26931,
	title = {Medico Multimedia Task at MediaEval 2019},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task\&nbsp;is running for the third time as part of MediaEval 2019. This year, we have changed the task from anomaly detection in images of the gastrointestinal tract to focus on the automatic prediction of human semen quality based on videos. The purpose of this task is to aid in the assessment of male reproductive health by providing a quick and consisted method of analyzing human semen. In this paper, we describe the task in detail, give a brief description of the provided dataset, and discuss the evaluation process and the metrics used to rank the submissions of the participants.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {26959,
	title = {The Multimedia Satellite Task at MediaEval 2019: Estimation of Flood Severity},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {This paper provides a description of the Multimedia Satellite Task at MediaEval 2019. The main objective of the task is to extract complementary information associated with events which are present in Satellite Imagery and news articles.Due to their high socio-economic impact, we focus on flooding events and built upon the last two years of the Multimedia Satellite Task. Our task focuses this year on flood severity estimation and consists of three subtasks: (1) Image-based News Topic Disambiguation, (2) Multimodal Flood Level Estimation from news, (3) Classification of city-centered satellite sequences. The task moves forward the state of the art in flood impact assessment by concentrating on aspects that are important but are not generally studied by multimedia researchers.},
	author = {Bischke, Benjamin and Helber, Patrick and Brugman, Simon and Basar, Erkan and Zhao, Zhengyu and Larson, Martha and Pogorelov, Konstantin}
}
@inproceedings {ahmad2019multi,
	title = {Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences},
	journal = {Multimediaeval Benchmark 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Ullah, Mohib and Riegler, Michael and Conci, Nicola and Langguth, Johannes and Al-Fuqaha, Ala}
}
@article {27107,
	title = {Natural disasters detection in social media and satellite imagery: a survey},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	year = {2019},
	month = {Jan-11-2019},
	pages = {31267 - 31302},
	publisher = { Springer},
	issn = {1380-7501},
	doi = {10.1007/s11042-019-07942-1},
	author = {Said, Naina and Ahmad, Kashif and Riegler, Michael and Pogorelov, Konstantin and Hassan, Laiq and Ahmad, Nasir and Conci, Nicola}
}
@inproceedings {26929,
	title = {Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features},
	journal = {MediaEval},
	year = {2019},
	publisher = {ceur ws org},
	abstract = {This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@article {ahmad2019social,
	title = {Social media and satellites: Disaster event detection, linking and summarization},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	number = {3},
	year = {2019},
	pages = {2837{\textendash}2875},
	publisher = {Springer},
	address = {Netherlands},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l}
}
@misc {25795,
	title = {Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches},
	howpublished = { IEEE Conference on Biomedical and Health Informatics (BHI) 2018},
	year = {2018},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. In this paper, we present several machine-learning-based approaches for angiectasia detection in wireless video capsule endoscopy images. The most promising results for pixel-wise localization and frame-wise detection are obtained by the proposed deep learning approach using generative adversarial networks (GANs) with a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection, which fits the requirements for automatic angiectasia detection in real clinical settings.},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and Espeland, H{\r a}vard and Kupka, Tomas and de Lange, Thomas and Griwodz, Carsten and Riegler, Michael}
}
@inproceedings {25950,
	title = {Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {490-493},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208113},
	author = {Hicks, Steven and Pogorelov, Konstantin and Lux, Mathias and Jeppsson, Mattis and Kristin Ranheim {Randel} and de Lange, Thomas and Sigrun Losadal {Eskeland} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {25794,
	title = {Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia},
	journal = {2018 IEEE Conference on Biomedical and Health Informatics (BHI)},
	year = {2018},
	pages = {365-368},
	publisher = {IEEE},
	abstract = {Angiectasia, formerly called angiodysplasia, is one of the most frequent vascular lesions and often the cause of gastrointestinal bleedings. Medical specialists assessing videos or images of examinations reach a detection performance of 16\% for the detection of bleeding to 69\% for the detection of angiectasia. This shows that automatic detection to support medical experts can be useful. In this paper, we present several machine learning-based approaches for angiectasia detection in wireless video capsule endoscopy frames. In summary, the most promising results for pixel-wise localization and framewise detection are obtained by the proposed deep learning method using generative adversarial networks (GANs). Using this approach, we achieve a sensitivity of 88\% and specificity of 99.9\% for pixel-wise localization, and a sensitivity of 98\% and a specificity of 100\% for frame-wise detection. Thus, the results demonstrate the capability of using deep learning for automatic angiectasia detection in real clinical settings.\&nbsp;},
	keywords = {Angiectasia, computer aided diagnosis, deep learning, Machine learning, video capsular endoscopy},
	doi = {10.1109/BHI.2018.8333444},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Petlund, Andreas and Halvorsen, P{\r a}l and de Lange, Thomas and Espeland, H{\r a}vard and Kupka, Tomas and Griwodz, Carsten and Riegler, Michael}
}
@inproceedings {25954,
	title = {Deep Learning and Hand-crafted Feature Based Approaches for Polyp Detection in Medical Videos},
	journal = {31st IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2018},
	pages = {381-386},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00073},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Jeppsson, Mattis and Espeland, H{\r a}vard and Griwodz, Carsten and de Lange, Thomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26259,
	title = {Deep learning approaches for flood classification and flood aftermath detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	volume = {2283},
	year = {2018},
	publisher = {CEUR-WS.org},
	address = {Sophia Antipolis, France},
	abstract = {This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events. In the first challenge, we mainly rely on object and scene level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are combined using early, late and double fusion techniques achieving an average F1-score of 65.03\%, 60.59\% and 63.58\%, respectively. For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30\% and 61.02\% for run 1 and run 2, respectively.},
	author = {Said, Naina and Pogorelov, Konstantin and Ahmad, Kashif and Riegler, Michael and Ahmad, Nasir and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola}
}
@inproceedings {25956,
	title = {Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding},
	journal = {31st IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2018},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00070},
	author = {Hicks, Steven and Riegler, Michael and Pogorelov, Konstantin and de Lange, Thomas and Kristin Ranheim {Randel} and Kim V. {{\r A}nonsen} and Jeppsson, Mattis and Halvorsen, P{\r a}l and Sigrun Losadal {Eskeland}}
}
@inproceedings {26250,
	title = {Efficient Live and on-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {81-88},
	publisher = {IEEE},
	address = {Taichung, Taiwan},
	abstract = {With 360{\textopenbullet} panorama video technology becoming commonplace, the need for efficient streaming methods for such videos arises. We go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user{\textquoteright}s field-of-view. We have created an architecture that combines RTP and DASH to deliver 360{\textopenbullet} VR content to a Huawei set-top-box and a Samsung Galaxy S7. Our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the field-of- view changes.\&nbsp;},
	doi = {10.1109/ISM.2018.00022},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Pogorelov, Konstantin and Riegler, Michael and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {26256,
	title = {Medico Multimedia Task at MediaEval 2018},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Hicks, Steven and Kristin Ranheim {Randel} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Ostroukhova, Olga and de Lange, Thomas}
}
@inproceedings {25953,
	title = {Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {369-374},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208129},
	author = {Hicks, Steven and Lux, Mathias and de Lange, Thomas and Kristin Ranheim {Randel} and Jeppsson, Mattis and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {25951,
	title = {OpenSea - Open Search Based Classification Tool},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {363-368},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208128},
	author = {Pogorelov, Konstantin and Albisser, Zeno and Ostroukhova, Olga and Lux, Mathias and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {26257,
	title = {Social Media and Satellites. Disaster event detection, linking and summarization},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	year = {2018},
	pages = {2837{\textendash}2875},
	publisher = {Springer},
	address = {US},
	abstract = {Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time.In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data.To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.},
	keywords = {Event Detection, Information retrieval, Natural Disaster, Social Media},
	doi = {10.1007/s11042-018-5982-9},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l}
}
@article {25860,
	title = {Top-Down Saliency Detection Driven by Visual Classification},
	journal = {Computer Vision and Image Understanding},
	volume = {172},
	year = {2018},
	pages = {67-76},
	publisher = {Elsevier},
	abstract = {This paper presents an approach for saliency detection able to emulate the integration of the top-down (task-controlled) and bottom-up (sensory information) processes involved in human visual attention. In particular, we first learn how to generate saliency when a specific visual task has to be accomplished. Afterwards, we investigate if and to what extentthe learned saliency maps can support visual classification in nontrivial cases. To achieve this, we propose SalClass-Net, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency mapsfrom input images, and b) the second one exploiting the computed saliency maps for visual classification.To test our approach, we collected a dataset of eye-gaze maps, using a Tobii T60 eye tracker, by asking several subjectsto look at images from the Stanford Dogs dataset, with the objective of distinguishing dog breeds.Performance analysis on our dataset and other saliency benchmarking datasets, such as POET, showed that Sal-ClassNet outperforms state-of-the-art saliency detectors, such as SalNet and SALICON. Finally, we also analyzedthe performance of SalClassNet in a fine-grained recognition task and found out that it yields enhanced classificationaccuracy compared to Inception and VGG-19 classifiers. The achieved results, thus, demonstrate that 1) condition-ing saliency detectors with object classes reaches state-of-the-art performance, and 2) explicitly providing top-downsaliency maps to visual classifiers enhances accuracy.},
	doi = {10.1016/j.cviu.2018.03.005},
	author = {Murabito, Francesca and Spampinato, Concetto and Palazzo, Simone and Giordano, Daniela and Pogorelov, Konstantin and Riegler, Michael}
}
@inproceedings {26249,
	title = {Tradeoffs using Binary and Multiclass Neural Network Classification for Medical Multidisease Detection},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {1-8},
	publisher = {IEEE},
	abstract = {The interest in neural networks has increased sig- nificantly, and the application of this type of machine learning is vast, ranging from natural image classification to medical image segmentation. However, many users of neural networks tend to use them as a black box tool. They do not access all of the possible variations, nor take into account the respective classification accuracies and costs. In our work, we focus on multiclass image classification, and in this research, we shed light on the trade-offs between systems using a single multiclass classification and multiple binary classifiers, respectively. We have tested the these classifiers on several modern neural network architectures, including DenseNet, Inception v3, Inception ResNet v2, Xception, NASNet and MobileNet. We have compared several aspects of the performance of these architectures during training and testing using both classification styles. We have compared classification speed and several classification accuracy metrics. Here, we present the results from experiments on a total of 99 networks: 11 multiclass and 88 individual binary networks, for an 8-class classification of medical images. In short, using multiple binary classification networks resulted in a 7\% increase in the average F1 score, a 1\% increase in average accuracy, a 1\% increase in precision, and a 4\% increase in average recall. However, on average, such a multi-network style performed the classification 7.6 times slower compared to a single network multiclass implementation. These collective findings show that both approaches can be applied to modern neural network structures. Several binary networks will often give increased classification accuracy, but at the cost of classification speed and resource consumption.\&nbsp;},
	doi = {10.1109/ISM.2018.00009},
	author = {Tor Jan Derek {Berstad} and Riegler, Michael and Espeland, H{\r a}vard and de Lange, Thomas and Pia H. {Smedsrud} and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l}
}
@inproceedings {26258,
	title = {Transfer learning with prioritized classification and training dataset equalization for medical objects detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {This paper presents the method proposed by the organizer team (SIMULA) for MediaEval 2018 Multimedia for Medicine: the Medico Task. We utilized the recent transfer-learning-based image classification methodology and focused on how easy it is to implement multi-class image classifiers in general and how to improve the classification performance without deep neural network model redesign. The goal for this was both to provide a baseline for the Medico task and to show the\&nbsp; performance of out-of-the-box classifiers for the medical use-case scenario.},
	author = {Ostroukhova, Olga and Pogorelov, Konstantin and Riegler, Michael and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l}
}
@inproceedings {25268,
	title = {ClusterTag: Interactive Visualization, Clustering and Tagging Tool for Big Image Collections},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
@inproceedings {25629,
	title = {CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l},
	editor = {Ahmad, Kashif}
}
@article {25292,
	title = {Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge},
	journal = {IEEE Transactions on Medical Imaging},
	year = {2017},
	pages = {1-19},
	publisher = {IEEE},
	keywords = {Endoscopic vision, Handcrafted features, Machine learning, Polyp Detection, Validation Framework},
	issn = {0278-0062},
	doi = {10.1109/TMI.2017.2664042},
	author = {Bernal, Jorge and Tajbakhsh, Nima and Sanchez, Javier and Bogdan J {Matuszewski} and Chen, Hao and Yu, Lequan and Angermann, Quentin and Romain, Olivier and Rustad, Bjorn and Balasingham, Ilangko and Pogorelov, Konstantin and Choi, Sungbin and Debard, Quentin and Maier-Hein, Lena and Speidel, Stefanie and Stoyanov, Danail and Brandao, Patrick and C{\'o}rdova, Henry and S{\'a}nchez-Montes, Cristina and Suryakanth R. {Gurudu} and Fern{\'a}ndez-Esparrach, Gloria and Dray, Xavier and Liang, Jianming and Histace, Aymeric}
}
@inproceedings {25628,
	title = {A comparison of deep learning with global features for gastrointestinal disease detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Olga, Ostroukhova and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {Suman:2017:DCB:3095713.3095731,
	title = {Detection and Classification of Bleeding Region in WCE Images Using Color Feature},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing},
	year = {2017},
	pages = {17:1{\textendash}17:6},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Wireless capsule endoscopy (WCE) is a modern and efficient technology to diagnose complete gastrointestinal tract (GIT) for various abnormalities. Due to long recording time of WCE, it acquires a huge amount of images, which is very tedious for clinical expertise to inspect each and every frame of a complete video footage. In this paper, an automated color feature based technique of bleeding detection is proposed. In case of bleeding, color is a very important feature for an efficient information extraction. Our algorithm is based on statistical color feature analysis and we use support vector machine (SVM) to classify WCE video frames into bleeding and non-bleeding classes with a high processing speed. An experimental evaluation shows that our method has promising bleeding detection performance with sensitivity and specificity higher than existing approaches.},
	keywords = {Bleeding detection, Color feature, Support vector machine, Wireless Capsule Endoscopy},
	isbn = {978-1-4503-5333-5},
	doi = {10.1145/3095713.3095731},
	url = {http://doi.acm.org/10.1145/3095713.3095731},
	author = {Suman, Shipra and Fawnizu Azmadi B. {Hussin} and Aamir Saeed {Malik} and Pogorelov, Konstantin and Riegler, Michael and Shaiw Hooi {Ho} and Hilmi, Ida and Khean Lee {Goh}}
}
@article {25462,
	title = {Efficient disease detection in gastrointestinal videos {\textendash} global features versus neural networks},
	journal = {Multimedia Tools and Applications},
	volume = {76},
	year = {2017},
	month = {11/2017},
	pages = {22493{\textendash}22525},
	publisher = {ACM/Springer},
	abstract = {Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.},
	keywords = {Algorithmic screening, Automatic disease detection, Deep learning neural networks, Global and local image features, Information retrieval, Medical, performance evaluation},
	issn = {1380-7501},
	doi = {10.1007/s11042-017-4989-y},
	url = {https://link.springer.com/article/10.1007\%2Fs11042-017-4989-y},
	author = {Pogorelov, Konstantin and Riegler, Michael and Sigrun Losada {Eskeland} and de Lange, Thomas and Johansen, Dag and Griwodz, Carsten and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l}
}
@inproceedings {25338,
	title = {EIR: changing the scene of automatic detection software for gastrointestinal endoscopy},
	journal = {World Congress of GI Endoscopy},
	year = {2017},
	month = {02/2017},
	publisher = {World Endoscopic Organisation},
	address = {Hyderabad, India},
	author = {Sigrun L. {Eskeland} and Aabakken, Lars and Riegler, Michael and Pogorelov, Konstantin and Peter Thelin {Schmidt} and Johansen, Dag and Halvorsen, P{\r a}l},
	editor = {de Lange, Thomas}
}
@article {25265,
	title = {From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {13},
	year = {2017},
	publisher = {ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Sigrun L. {Eskeland} and Peter Thelin {Schmidt} and Albisser, Zeno and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {25433,
	title = {A Holistic Multimedia System for Gastrointestinal Tract Disease Detection},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {112-123},
	publisher = {ACM},
	abstract = {Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.\&nbsp;In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083189},
	url = {http://dl.acm.org/citation.cfm?id=3083189},
	author = {Pogorelov, Konstantin and Sigrun Losadal {Eskeland} and de Lange, Thomas and Griwodz, Carsten and Kristin Ranheim {Randel} and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Spampinato, Concetto and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
@inproceedings {25812,
	title = {JORD: A System for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing  - CBMI {\textquoteright}17},
	year = {2017},
	pages = {12:1--12:6},
	publisher = {ACM Press},
	address = {New York, USA},
	abstract = {Gathering information, and continuously monitoring the affected areas after a natural disaster can be crucial to assess the damage, and speed up the recovery process. Satellite imagery is being considered as one of the most productive sources to monitor the after effects of a natural disaster; however, it also comes with a lot of challenges and limitations, due to slow update. It would be beneficiary to link remote sensed data with social media for the damage assessment, and obtaining detailed information about a disaster. The additional information, which are obtainable by social media, can enrich remote-sensed data, and overcome its limitations. To tackle this, we present a system called JORD that is able to autonomously collect social media data about natural disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. We also provide content based analysis along with temporal and geo-location based filtering to provide more accurate information to the users. To show the capabilities of the system, we demonstrate that a large number of disaster events can be detected by the system. In addition, we use crowdsourcing to demonstrate the quality of the provided information about the disasters, and usefulness of JORD from potential users point of view.},
	isbn = {9781450353335},
	doi = {10.1145/3095713.3095726},
	url = {http://dl.acm.org/citation.cfm?doid=3095713},
	author = {Ahmad, Kashif and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and De Natale, Francesco}
}
@inproceedings {25332,
	title = {Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection},
	journal = {ACM Multimedia Systems},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	address = {Taiwan},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and Griwodz, Carsten and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Peter Thelin {Schmidt} and Spampinato, Concetto and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25627,
	title = {Multimedia for medicine: the medico Task at mediaEval 2017},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@inproceedings {25333,
	title = {Nerthus: A Bowel Preparation Quality Video Dataset},
	journal = {ACM Multimedia Systems},
	year = {2017},
	publisher = {ACM},
	address = {Taipei},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Griwodz, Carsten and Spampinato, Concetto and Taschwer, Mario and Lux, Mathias and Peter Thelin {Schmidt} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {24472,
	title = {Computer Aided Disease Detection System for Gastrointestinal Examinations},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Pogorelov, Konstantin and Markussen, Jonas and Lux, Mathias and H{\r a}kon Kvale {Stensland} and de Lange, Thomas and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24474,
	title = {Efficient Processing of Videos in a Multi Auditory Environment Using Device Lending of GPUs},
	journal = {The 7th International Conference on Multimedia Systems (MMSys)},
	year = {2016},
	month = {05/2016},
	pages = {36:1-36:4},
	publisher = {ACM},
	isbn = {978-1-4503-4297-1},
	doi = {10.1145/2910017.2910636},
	author = {Pogorelov, Konstantin and Riegler, Michael and Markussen, Jonas and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun L. {Eskeland} and de Lange, Thomas}
}
@inproceedings {24482,
	title = {EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and de Lange, Thomas and Griwodz, Carsten and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24481,
	title = {Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Lux, Mathias and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun L. {Eskeland} and de Lange, Thomas}
}
@inproceedings {24478,
	title = {GPU-accelerated Real-time Gastrointestinal Diseases Detection},
	journal = {CBMS 2016 : The 29th International Symposium on Computer-Based Medical Systems},
	year = {2016},
	month = {07/2016},
	publisher = {IEEE},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas and Peter Thelin {Schmidt} and Sigrun L. {Eskeland} and Griwodz, Carsten and Johansen, Dag}
}
@inproceedings {24541,
	title = {Heimdallr: a dataset for sport analysis},
	journal = {ACM Multimedia System},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	abstract = {In this paper, we present Heimdallr, a dataset that aims to serve two different purposes. The first purpose is action recognition and pose estimation, which requires a dataset of annotated sequences of athlete skeletons. We employed a crowdsourcing platform where people around the world were asked to annotate frames and obtained more than 3000 fully annotated frames for 42 different sequences with a variety of poses and actions. The second purpose is an improved understanding of crowdworkers, and for this purpose, we collected over 10000 written feedbacks from 592 crowdworkers. This is valuable information for crowdsourcing researchers who explore algorithms for worker quality assessment. In addition to the complete dataset, we also provide the code for the application that has been used to collect the data as an open source software.},
	doi = {http://dl.acm.org/citation.cfm?doid=2910017.2910621},
	author = {Riegler, Michael and Dang-Nguyen, Duc-Tien and Winther, B{\r a}rd and Griwodz, Carsten and Pogorelov, Konstantin and Halvorsen, P{\r a}l}
}
@inproceedings {24475,
	title = {LIRE - Open Source Visual Information Retrieval},
	journal = {Multimedia System Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Lux, Mathias and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Anagnostopoulos, Nektarios}
}
@inproceedings {24641,
	title = {Multimedia and Medicine: Teammates for Better Disease Detection and Survival},
	journal = {ACM Multimedia},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	address = {Amsterdam, The Netherlands, The Netherlands},
	doi = {http://dx.doi.org/10.1145/2964284.2976760},
	author = {Riegler, Michael and Griwodz, Carsten and Spampinato, Concetto and de Lange, Thomas and Sigrun L. {Eskeland} and Pogorelov, Konstantin and Tavanapong, Wallapak and Peter Thelin {Schmidt} and Gurrin, Cathal and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l}
}
@inproceedings {24473,
	title = {Right inflight? A dataset for exploring the automatic prediction of movies suitable for a watching situation},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Spampinato, Concetto and Halvorsen, P{\r a}l and Lux, Mathias and Markussen, Jonas and Pogorelov, Konstantin and Griwodz, Carsten and H{\r a}kon Kvale {Stensland}}
}
@inproceedings {24858,
	title = {Simula @ MediaEval 2016 Context of Experience Task},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {This paper presents our approach for the Context of Multimedia Experience Task of the MediaEval 2016 Benchmark. We present different analyses of the given data using different subsets of data sources and combinations of it. Our approach gives a baseline evaluation indicating that metadata approaches work well but that also visual features can provide useful information for the given problem to solve.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
