755 citations · 1.2k across the 42 of their papers we have counts for
9 papers · 1 filter
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,…
Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods
Eva Prakash, Maayane Attias, Pierre Chambon +5
Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against…
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Process Reward Models for Sentence-Level Verification of LVLM Radiology Reports
Alois Thomas, Maya Varma, Jean-Benoit Delbrouck +1
Automating radiology report generation with Large Vision-Language Models (LVLMs) holds great potential, yet these models often produce clinically critical hallucinations, posing se…
RadEval: A framework for radiology text evaluation
Justin Xu, Xi Zhang, Javid Abderezaei +9
We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics, from classic n-gram overlap (BLEU, ROUGE) an…
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Stefania L. Moroianu, Christian Bluethgen, Pierre Chambon +8
Achieving robust performance and fairness across diverse patient populations remains a challenge in developing clinically deployable deep learning models for diagnostic imaging. Sy…
Structuring Radiology Reports: Challenging LLMs with Lightweight Models
Johannes Moll, Louisa Fay, Asfandyar Azhar +5
Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While…
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…
SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning
Melanie Rieff, Maya Varma, Ossian Rabow +9
Multimodal in-context learning (ICL) remains underexplored despite significant potential for domains such as medicine. Clinicians routinely encounter diverse, specialized tasks req…