From the 1 of 16 linked papers with an AI index.
23 citations · 23 across the 7 of their papers we have counts for
8 papers · 1 filter
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier +2
The paper presents Symbal, a dual‑stage method that uses off‑the‑shelf foundation models to automatically detect systematic misalignments—recurring caption errors tied to specific…
CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography
Eva Prakash, Yunhe Gao, Chong Wang +10
Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-report pairs and lack explic…
A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation
Yabin Zhang, Chong Wang, Yunhe Gao +19
Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic erro…
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…
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…