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29 May 2024 Longitudinal interpretability of deep learning-based breast cancer risk prediction model: comparison of different attribution methods
Zan Klanecek, Yao-Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Brayden Schott, Ali Deatsch, Andrej Studen, Kristijana Hertl, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Hilde Bosmans, Robert Jeraj
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Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741W (2024) https://doi.org/10.1117/12.3025858
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States
Abstract
When developing Deep Learning models intended for clinical applications, understanding which part of the input contributed the most to the final decision is crucial. Our study brings interpretability to a Breast Cancer Risk (BCR) prediction by exploring whether the model relies on the laterality of the breast, where cancer ultimately develops, and how this reliance evolves over time. A dataset of 1210 Full-Field-Digital-Mammography exams with 0 to 7 Years To Cancer was used. MIRAI model was employed for BCR predictions. To determine which side of the breast contributed the most to the BCR prediction, the signal difference between left and right breasts was calculated for eight attribution-based interpretability techniques. AUC was calculated to investigate whether the BCR prediction is predominantly made from the breast, where the cancer ultimately develops. For 0 to 1 Years To Cancer, the model predominantly predicts BCR based on the side of the breast where the cancer is already present AUC=0.92 to 0.95. The top-performing attribution methods achieved an AUC of 0.70 for mammograms captured 1 to 3 Years To Cancer. For exams that were 3 to 5 Years To Cancer, a significant drop to AUC of 0.57 was observed. When moving to 5 to 7 Years To Cancer, focus on the breast with future cancer becomes random. All attribution methods showed that BCR predictions extending beyond three years from screen-detected cancer are most likely based on typical breast characteristics, such as density and other long-standing tissue patterns; however, for short-term BCR predictions, the model seems to detect early signs of tumor development.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zan Klanecek, Yao-Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Brayden Schott, Ali Deatsch, Andrej Studen, Kristijana Hertl, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Hilde Bosmans, and Robert Jeraj "Longitudinal interpretability of deep learning-based breast cancer risk prediction model: comparison of different attribution methods", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741W (29 May 2024); https://doi.org/10.1117/12.3025858
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KEYWORDS
Cancer
Breast
Breast cancer
Mammography
Cancer detection
Deep learning
Medical physics
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