44 citations · 46 across the 5 of their papers we have counts for
5 papers
Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting
Shantanu Ghosh, Vedant Parthesh Joshi, Rayan Syed +14
Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest…
Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang +27
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3…
Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics
Nihal Murali, Aahlad Puli, Ke Yu +2
Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model gener…
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Yanwu Xu, Mingming Gong, Shaoan Xie +1
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe ann…
Explaining the Black-box Smoothly- A Counterfactual Approach
Sumedha Singla, Motahhare Eslami, Brian Pollack +2
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess fea…