160 citations · 198 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
Chiyu Ma, Brandon Zhao, Chaofan Chen +1
We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-base…
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao +4
When we deploy machine learning models in high-stakes medical settings, we must ensure these models make accurate predictions that are consistent with known medical science. Inhere…