Abstract
High-risk atypical breast lesions are a notoriously difficult dilemma for pathologists who diagnose breast biopsies in breast cancer screening programs. We reframe the computational diagnosis of atypical breast lesions as a problem of prototype recognition on the basis that pathologists mentally relate current histological patterns to previously encountered patterns during their routine diagnostic work. In an unsupervised manner, we investigate the relative importance of ductal (global) and intraductal patterns (local) in a set of pre-selected prototypical ducts in classifying atypical breast lesions. We conducted experiments to test this strategy on subgroups of breast lesions that are a major source of inter-observer variability; these are benign, columnar cell changes, epithelial atypia, and atypical ductal hyperplasia in order of increasing cancer risk. Our model is capable of providing clinically relevant explanations to its recommendations, thus it is intrinsically explainable, which is a major contribution of this work. Our experiments also show state-of-the-art performance in recall compared to the latest deep-learning based graph neural networks (GNNs).
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Acknowledgments
The grant NIH-NCI U01CA204826 to SCC supported this work. The work of AP and OC was partially supported by the sub-contracts 9F-60178 and 9F-60287 from Argonne National Laboratory (ANL) to the University of Pittsburgh from the parent grant DE-AC02-06CH1135 titled, Co-Design of Advanced Artificial Intelligence Systems for Predicting Behavior of Complex Systems Using Multimodal Datasets, from the Department of Energy to ANL.
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Parvatikar, A. et al. (2021). Prototypical Models for Classifying High-Risk Atypical Breast Lesions. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_14
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DOI: https://doi.org/10.1007/978-3-030-87237-3_14
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