activity
20232025
most citedFoundation Models in Radiology: What, How, When, Why and Why Not

90 citations · 97 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Automated Structured Radiology Report Generation

Jean-Benoit Delbrouck, Justin Xu, Johannes Moll +11

Automated radiology report generation from chest X-ray (CXR) images has the potential to improve clinical efficiency and reduce radiologists' workload. However, most datasets, incl…

cs.CV2024

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…

cs.LG202490 cited

Foundation Models in Radiology: What, How, When, Why and Why Not

Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…

cs.CL20247 cited

CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats

Pierre Chambon, Jean-Benoit Delbrouck, Thomas Sounack +6

Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language mo…

cs.CV2023

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data

Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper +2

Vision-language models (VLMs), such as CLIP and ALIGN, are generally trained on datasets consisting of image-caption pairs obtained from the web. However, real-world multimodal dat…