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A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging." Medical Image Analysis 70 (2021): 102007."
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation." IEEE Journal of Biomedical and Health Informatics 25, no. 6 (2021): 2029-2040."
DeepSynthBody: the beginning of the end for data deficiency in medicine In The International Conference on Applied Artificial Intelligence (ICAPAI). IEEE, 2021.
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy In 27th International Conference on Multimedia Modeling. Vol. LNCS, volume 12573. Springer, 2021.
Medico Multimedia Task at MediaEval 2021: Transparency in Medical Image Segmentation In Mediaeval Medico 2021. Mediaeval 2021, 2021.
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy In 34th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2021.
Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy In International Conference on Multimedia Modeling. Springer, 2020.
Kvasir-SEG: A Segmented Polyp Dataset In International Conference on Multimedia Modeling. Daejeon, Korea: Springer, 2020.
Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation In Medico MediaEval 2020. CEUR, 2020.
Deep Learning for Automatic Generation of Endoscopy Reports." Gastrointestinal Endoscopy 89, no. 6 (2019)."
Efficient Processing of Medical Videos in a Multi-auditory Environment Using Gpu Lending. NVIDIA's GPU Technology Conference (GTC), 2019.
Kunstig intelligens for endoskopi – Automatisk deteksjon av lesjoner i sanntid. NGF Nytt, Vol. 26, No 1, March 2019, p. 34: Norsk Gastroenterologisk Forening, 2019.
Maskinlæringssystemer for gastrointestinale endoskopier." Best Practice Nordic - Gastroenterologi (2019)."
ResUNet++: An Advanced Architecture for Medical Image Segmentation In 2019 IEEE International Symposium on Multimedia (ISM). San Diego, California, USA: IEEE, 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.
Tradeoffs using Binary and Multiclass Neural Network Classification for Medical Multidisease Detection In 2018 IEEE International Symposium on Multimedia (ISM). IEEE, 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."