@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}
}
@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 {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 {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 {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 {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 {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}
}
