Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations - researchr publication authors

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Lior Ness, Ella Barkan, Michal Ozery-Flato. Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations. In Jaime S. Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi-Sureshjani, editors, Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Volume 12446 of Lecture Notes in Computer Science, pages 33-42, Springer, 2020. [doi]

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