Publications [#380361] of Cynthia D. Rudin

Papers Published

1. Yang, J; Barnett, AJ; Donnelly, J; Kishore, S; Fang, J; Schwartz, FR; Chen, C; Lo, JY; Rudin, C, FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (January, 2024), pp. 5003-5009 [doi]
   (last updated on 2025/07/03)

   Abstract:
   Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.