@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 {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}
}
@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 {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 {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 {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}
}
@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}
}
