@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 {27867,
	title = {Kvasir-Capsule, a video capsule endoscopy dataset},
	journal = {Scientific Data},
	volume = {8},
	year = {2021},
	pages = {142},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present\&nbsp;Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital.\&nbsp;Kvasir-Capsule\&nbsp;consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The\&nbsp;Kvasir-Capsule\&nbsp;dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
	doi = {10.1038/s41597-021-00920-z},
	url = {http://www.nature.com/articles/s41597-021-00920-z},
	author = {Pia H. {Smedsrud} and Thambawita, Vajira and Hicks, Steven and Gjestang, Henrik and Oda Olsen {Nedrejord} and N{\ae}ss, Espen and Borgli, Hanna and Jha, Debesh and Tor Jan Derek {Berstad} and Sigrun L. {Eskeland} and Lux, Mathias and Espeland, H{\r a}vard and Petlund, Andreas and Duc Tien Dang {Nguyen} and Garcia-Ceja, Enrique and Johansen, Dag and Peter T. {Schmidt} and Toth, Ervin and Hugo Lewi {Hammer} and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@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 {27333,
	title = {PMData: a sports logging dataset},
	journal = {Proceedings of the 11th ACM Multimedia Systems Conference},
	year = {2020},
	pages = {231-236},
	publisher = {ACM},
	author = {Thambawita, Vajira and Hicks, Steven and Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Jha, Debesh and Martin Kristoffer {Svensen} and Pettersen, Svein-Arne and Johansen, Dag and H{\r a}vard Dagenborg {Johansen} and Susann Dahl {Pettersen} and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr{\o}nli, Tor-Morten and Per Morten {Fredriksen} and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi{\o}rn-Hansen, Andreas and Duc Tien Dang {Nguyen} and Kupka, Tomas and Hugo Lewi {Hammer} and Jain, Ramesh and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {borgli19ISMICT,
	title = {Automatic Hyperparameter Optimization for Transfer Learning on Medical Image Datasets Using Bayesian Optimization},
	journal = {13th International Symposium on Medical Information and Communication Technology (ISMICT)},
	year = {2019},
	pages = {1-6},
	publisher = {IEEE},
	doi = {10.1109/ISMICT.2019.8743779},
	author = {Borgli, Rune and H{\r a}kon Kvale {Stensland} 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}
}
@article {26754,
	title = {Flexible device compositions and dynamic resource sharing in PCIe interconnected clusters using Device Lending},
	journal = {Cluster Computing},
	volume = {22},
	year = {2019},
	month = {09/2019},
	pages = {1-24},
	publisher = { Springer},
	abstract = {Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers interconnected in a cluster, access to remote hardware resources often requires facilitation both in hardware and specialized drivers with virtualization support. This limits the availability of resources to specific devices and drivers that are supported by the virtualization technology being used, as well as what the interconnection technology supports. For PCI Express (PCIe) clusters, we have previously proposed Device Lending as a solution for enabling direct low latency access to remote devices. The method has extremely low computing overhead and does not require any application- or device-specific distribution mechanisms. Any PCIe device, such as network cards disks, and GPUs, can easily be shared among the connected hosts. In this work, we have extended our solution with support for a virtual machine (VM) hypervisor. Physical remote devices can be {\textquotedblleft}passed through{\textquotedblright} to VM guests, enabling direct access to physical resources while still retaining the flexibility of virtualization. Additionally, we have also implemented multi-device support, enabling shortest-path peer-to-peer transfers between remote devices residing in different hosts. Our experimental results prove that multiple remote devices can be used, achieving bandwidth and latency close to native PCIe, and without requiring any additional support in device drivers. I/O intensive workloads run seamlessly using both local and remote resources. With our added VM and multi-device support, Device Lending offers highly customizable configurations of remote devices that can be dynamically reassigned and shared to optimize resource utilization, thus enabling a flexible composable I/O infrastructure for VMs as well as bare-metal machines.},
	issn = {1573-7543},
	doi = {10.1007/s10586-019-02988-0},
	url = {https://link.springer.com/article/10.1007/s10586-019-02988-0},
	author = {Markussen, Jonas and Lars Bj{\o}rlykke {Kristiansen} and Borgli, Rune and H{\r a}kon Kvale {Stensland} and Seifert, Friedrich and Riegler, Michael and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {26820,
	title = {Saga: An Open Source Platform for Training Machine Learning Models and Community-driven Sharing of Techniques},
	journal = {International Conference on Content-Based Multimedia Indexing (CBMI 2019)},
	year = {2019},
	pages = {1-4},
	publisher = {IEEE},
	doi = {10.1109/CBMI.2019.8877455},
	author = {Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26547,
	title = {Unsupervised Preprocessing to Improve Generalisation for Medical Image Classification},
	journal = {IEEE 13th International Symposium on Medical Information and Communication Technology (ISMICT)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Automated disease detection in videos and images from the gastrointestinal (GI) tract has received much attention in the last years. However, the quality of image data is often reduced due to overlays of text and positional data.\&nbsp;In this paper, we present different methods of preprocessing such images and we describe our approach to GI disease classification for the Kvasir v2 dataset.\&nbsp;We propose multiple approaches to inpaint problematic areas in the images to improve the anomaly classification, and we discuss the effect that such preprocessing does to the input data.In short, our experiments show that the proposed methods improve the Matthews correlation coefficient by approximately 7\% in terms of better classification of GI anomalies.},
	doi = {10.1109/ISMICT.2019.8743979},
	author = {Kirker{\o}d, Mathias and Borgli, Rune and Thambawita, Vajira and Hicks, Steven and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {runeMedico2018,
	title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	abstract = {This paper details the approach to the MediaEval 2018 Medico Multimedia Task made by the Rune team. The decided upon approach uses a work-in-progress hyperparameter optimization system called Saga. Saga is a system for creating the best hyperparameter finding in Keras, a popular machine learning framework, using Bayesian optimization and transfer learning. In addition to optimizing the Keras classifier configuration, we try manipulating the dataset by adding extra images in a class lacking in images and splitting a commonly misclassified class into two classes.},
	keywords = {automatic hyperparameter optimization, Bayesian optimization, CNN, convolutional neural networks, dataset manipulation, gpyopt, hyperparameter optimization, keras, saga, tensorflow, Transfer Learning},
	author = {Borgli, Rune and Halvorsen, P{\r a}l and Riegler, Michael and H{\r a}kon Kvale {Stensland}}
}
@mastersthesis {26253,
	title = {Hyperparameter optimization using Bayesian optimization on transfer learning for medical image classification},
	journal = {The University of Oslo},
	year = {2018},
	abstract = {The field of medicine has a history of adopting new technology. Video equipment and sensors are used to visualize areas of interest in the human allowing for doctors to make diagnoses based on imagery observations. However, the detection rate of the doctors towards diseases and abnormalities is heavily dependent on the experience and state of mind of the doctor doing the examination. Computer-aided detection systems are systems designed to aid the doctor in improving the detection rate, and they are using or experimenting with machine learning. Deep convolutional neural networks, a type of machine learning, are shown to be highly efficient at image detection, classification, and analysis. However, these networks require large datasets to train properly. Transfer learning is a training technique where we use a pre-trained machine learning model and transfer some of the attained knowledge from other application domains over to a new model. This way, we can use small datasets and train a model in much shorter time. In this respect, transfer learning works fine but has many configurations called hyperparameters which are often not optimized. Our work aims to address the lack of automatic hyperparameter optimization for transfer learning by experiments utilizing a known hyperparameter optimization method and creating a system for running those experiments. First, we decided to focus on the field of gastroenterology by utilizing two publicly available datasets showing images from the gastrointestinal tract. Next, we used a specific transfer learning method and chose hyperparameters suitable for automatic optimization. The optimization method we chose was Bayesian optimization because of its reputation for being one of the best methods for hyperparameter optimization. However, Bayesian optimization has hyperparameters of its own, and there are also different versions of Bayesian optimization. We chose to limit the thesis, so we use standard Bayesian optimization with standard parameters. We created a system for running automatic experiments of three different hyperparameter optimization strategies. With the system, we ran a set of experiments for each dataset. Between the strategies, one was successful in achieving a high validation accuracy, while the others were considered failures. Compared to baselines, our best models was around 10\% better. With these experiments, we demonstrated that automatic hyperparameter optimization is an effective strategy for increasing performance in transfer learning and that the best hyperparameters are nontrivial to select manually.},
	keywords = {Bayesian optimization, convolutional networks, deep learning, hyperparameter optimization, Machine learning, nonconvergence filtering, Transfer Learning},
	url = {https://www.duo.uio.no/handle/10852/64146},
	author = {Borgli, Rune}
}
