5 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
RadDiff: Describing Differences in Radiology Image Sets with Natural Language
Xiaoxian Shen, Yuhui Zhang, Sahithi Ankireddy +5
Understanding how two radiology image sets differ is critical for generating clinical insights and for interpreting medical AI systems. We introduce RadDiff, a multimodal agentic s…
TRoVe: Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models
Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier +2
Vision-language models (VLMs) have made great strides in addressing temporal understanding tasks, which involve characterizing visual changes across a sequence of images. However,…
From Detection to Mitigation: Addressing Bias in Deep Learning Models for Chest X-Ray Diagnosis
Clemence Mottez, Louisa Fay, Maya Varma +2
Deep learning models have shown promise in improving diagnostic accuracy from chest X-rays, but they also risk perpetuating healthcare disparities when performance varies across de…
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models
Maya Varma, Jean-Benoit Delbrouck, Zhihong Chen +2
Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time…