108 citations · 249 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
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…
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning
Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna +4
Purpose: To develop high throughput multi-label annotators for body (chest, abdomen, and pelvis) Computed Tomography (CT) reports that can be applied across a variety of abnormalit…