90 citations · 209 across the 26 of their papers we have counts for
28 papers
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…
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…
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…
Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Christian Bluethgen, Dave Van Veen, Daniel Truhn +8
Building agents, systems that perceive and act upon their environment with a degree of autonomy, has long been a focus of AI research. This pursuit has recently become vastly more…
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…
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…
MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
Maya Varma, Ashwin Kumar, Rogier van der Sluijs +7
Medical images are acquired at high resolutions with large fields of view in order to capture fine-grained features necessary for clinical decision-making. Consequently, training d…
Best Practices for Large Language Models in Radiology
Christian Bluethgen, Dave Van Veen, Cyril Zakka +7
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language…
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…
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…
CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback
Dennis Hein, Zhihong Chen, Sophie Ostmeier +8
Radiologists play a crucial role in translating medical images into actionable reports. However, the field faces staffing shortages and increasing workloads. While automated approa…
Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
Justin Xu, Zhihong Chen, Andrew Johnston +9
Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in…