160 citations · 193 across the 4 of their papers we have counts for
4 papers
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…
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
Chaofan Chen, Kangcheng Lin, Cynthia Rudin +3
Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support to…
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao +4
Interpretability in machine learning models is important in high-stakes decisions, such as whether to order a biopsy based on a mammographic exam. Mammography poses important chall…
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Oscar Li, Hao Liu, Chaofan Chen +1
Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of…