FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
<|reference_start|>FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography: 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.<|reference_end|>
@article{yang2024fpn-iaia-bl:,
    title={FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for
  Classification of Mass Margins in Digital Mammography},
    author={Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry
  Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin},
    journal={arXiv preprint arXiv:2406.06386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06386},
    primaryClass={cs.CV}
}