90 citations · 209 across the 26 of their papers we have counts for
10 papers · 1 filter
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier +2
Multimodal large language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs. Our work focuses on a class of captioning…
BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases
Qi Chen, Wenxuan Li, Pedro R. A. S. Bassi +14
Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsistently across real-world clinic…
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…
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Sonali Sharma, Jin Long, George Shih +7
Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current vision-language models are prim…
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…
Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models
Yabin Zhang, Maya Varma, Yunhe Gao +4
Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from…
Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision
Yunhe Gao, Yabin Zhang, Chong Wang +5
Foundation models have transformed vision and language by learning general-purpose representations from large-scale unlabeled data, yet 3D medical imaging lacks analogous approache…
A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Chong Wang, Yabin Zhang, Yunhe Gao +9
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical…
Gradient-Stable Attention Heads Signal LLM Correctness
Sophie Ostmeier, Brian Axelrod, Maya Varma +6
Large language models (LLMs) often generate plausible yet incorrect answers, posing risks in safety-critical settings such as medicine. Human evaluation is expensive, and LLM-as-ju…
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…