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Efficient Processing of Medical Videos in a Multi-auditory Environment Using Gpu Lending. Nvidia GTC, 2019.
Explainable AI for Automatic Generation of Endoscopy Reports. DDW 2019: None, 2019.
Kunstig intelligens for endoskopi – Automatisk deteksjon av lesjoner i sanntid. Norsk Gastroenterologisk Forening, 2019.
Automatic Detection of Angiectasia: Evaluation of Deep Learning and Handcrafted Approaches In IEEE Conference on Biomedical and Health Informatics (BHI) 2018., 2018.
Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis In Proceedings of the 9th ACM Multimedia Systems Conference. Amsterdam, Netherlands: ACM, 2018.
Deep Learning and Handcrafted Feature Based Approaches for Automatic Detection of Angiectasia In 2018 IEEE Conference on Biomedical and Health Informatics (BHI). IEEE, 2018.
Deep Learning and Hand-crafted Feature Based Approaches for Polyp Detection in Medical Videos In 31st IEEE CBMS International Symposium on Computer-Based Medical Systems. Karlstad, Sweden: IEEE, 2018.
Dissecting Deep Neural Networks for Better Medical Image Classification and Classification Understanding In 31st IEEE CBMS International Symposium on Computer-Based Medical Systems. Karlstad, Sweden: IEEE, 2018.
Medico Multimedia Task at MediaEval 2018 In Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR Workshop Proceedings, 2018.
Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy." World Journal of Gastroenterology 45, no. 24 (2018): 5057-5062."
Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain In Proceedings of the 9th ACM Multimedia Systems Conference. Amsterdam, Netherlands: ACM, 2018.
A comparison of deep learning with global features for gastrointestinal disease detection In MediaEval Benchmark 2017, Edited by M. Riegler. Dublin, Ireland: CEUR-WS.org, 2017.
Efficient disease detection in gastrointestinal videos – global features versus neural networks." Multimedia Tools and Applications 76, no. 21 (2017): 22493-22525."
EIR: changing the scene of automatic detection software for gastrointestinal endoscopy In World Congress of GI Endoscopy, Edited by T. de Lange. Hyderabad, India: World Endoscopic Organisation, 2017.
From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System." ACM Transactions on Multimedia Computing, Communications, and Applications 13, no. 3 (2017)."
A Holistic Multimedia System for Gastrointestinal Tract Disease Detection In 8th annual ACM conference on Multimedia Systems (MMSys), Edited by P. Cesar and C. Hsu. ACM, 2017.
Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection In ACM Multimedia Systems. Taiwan: ACM, 2017.
Multimedia for medicine: the medico Task at mediaEval 2017 In MediaEval Benchmark 2017, Edited by M. Riegler. Dublin, Ireland: CEUR-WS.org, 2017.
Nerthus: A Bowel Preparation Quality Video Dataset In ACM Multimedia Systems. Taipei: ACM, 2017.
Computer Aided Disease Detection System for Gastrointestinal Examinations In Multimedia Systems Conference 2016. New York: ACM, 2016.
Efficient Processing of Videos in a Multi Auditory Environment Using Device Lending of GPUs In The 7th International Conference on Multimedia Systems (MMSys). ACM, 2016.
EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies In International Workshop on Content-based Multimedia Indexing. IEEE / ACM, 2016.
Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery In International Workshop on Content-based Multimedia Indexing. IEEE / ACM, 2016.
GPU-accelerated Real-time Gastrointestinal Diseases Detection In CBMS 2016 : The 29th International Symposium on Computer-Based Medical Systems. IEEE, 2016.
Multimedia and Medicine: Teammates for Better Disease Detection and Survival In ACM Multimedia. Amsterdam, The Netherlands, The Netherlands: ACM, 2016.