560 citations · 617 across the 11 of their papers we have counts for
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cs.CV2024★ 1 cited
This Looks Better than That: Better Interpretable Models with ProtoPNeXt
Frank Willard, Luke Moffett, Emmanuel Mokel +6
Prototypical-part models are a popular interpretable alternative to black-box deep learning models for computer vision. However, they are difficult to train, with high sensitivity…
cs.CV2024★ 1 cited
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
Julia Yang, Alina Jade Barnett, Jon Donnelly +6
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-s…