Prototypical Models for Classifying High-Risk Atypical Breast Lesions

Akash Parvatikar, Om Choudhary, Arvind Ramanathan, Rebekah Jenkins, Olga Navolotskaia, Gloria Carter, Akif Burak Tosun, Jeffrey L. Fine, S. Chakra Chennubhotla

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).

External IDs:dblp:conf/miccai/ParvatikarCRJNC21