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S. Ali, D. Jha, N. Ghatwary, S. Realdon, R. Cannizzaro, O. E. Salem, D. Lamarque, C. Daul, K. V. Anonsen, M. Riegler et al.

A multi-center polyp detection and segmentation dataset for generalisability assessment

Nature Scientific Data 10 (2023).
Journal Article
2023
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N. K. Tomar, D. Jha, M. Riegler, H. D. Johansen, D. Johansen, J. Rittscher, P. Halvorsen and S. Ali

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

IEEE Transactions on Neural Networks and Learning Systems (2022): 1-14.
Journal Article
2022
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R. Khadka, D. Jha, S. Hicks, V. Thambawita, M. Riegler, S. Ali and P. Halvorsen

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Computers in Biology and Medicine 143 (2022): 105227.
Journal Article
2022
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A. Srivastava, D. Jha, S. Chanda, U. Pal, H. Johansen, D. Johansen, M. Riegler, S. Ali and P. Halvorsen

MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

IEEE Journal of Biomedical and Health Informatics 26, no. 5 (2022): 2252-2263.
Journal Article
2022
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2021

Journal articles

P. H. Smedsrud, V. Thambawita, S. Hicks, H. Gjestang, O. O. Nedrejord, E. Næss, H. Borgli, D. Jha, T. J. D. Berstad, S. L. Eskeland et al.

Kvasir-Capsule, a video capsule endoscopy dataset

Scientific Data 8, no. 1 (2021): 142.
Status: Published
2020

Journal articles

H. Borgli, V. Thambawita, P. H. Smedsrud, S. Hicks, D. Jha, S. L. Eskeland, K. R. Randel, K. Pogorelov, M. Lux, D. T. D. Nguyen et al.

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy

Scientific Data 7, no. 1 (2020): 1-14.
Status: Published
T. Roß, A. Reinke, P. M. Full, M. Wagner, H. Kenngott, M. Apitz, H. Hempe, D. M. Filimon, P. Scholz, T. N. Tran et al.

Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge

Medical Image Analysis (2020).
Status: Published
V. Thambawita, D. Jha, H. L. Hammer, H. D. Johansen, D. Johansen, P. Halvorsen and M. Riegler

An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification

ACM Transactions on Computing for Healthcare 1 (2020): 1-29.
Status: Published