10 papers
Sparse Concept Channels in Frozen 3D CT Vision Encoders
Farhad Nooralahzadeh, Lea Bogensperger, Christian Bluethgen +1
Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know <i>which</i> internal units encode clinical findings or <i>wh…
RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Mélanie Roschewitz, Kenneth Styppa, Yitian Tao +10
Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods large…
Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models
Farhad Nooralahzadeh, Benjamin Gundersen, Nicolas Deperrois +7
Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in the image, miss important ones…
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Sonia Laguna, Andrea Agostini, Alain Ryser +9
Building robust medical machine learning systems requires pretraining strategies that exploit the intrinsic structure present in clinical data. We introduce Multiview Masked Autoen…
Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning
Benjamin Gundersen, Nicolas Deperrois, Samuel Ruiperez-Campillo +5
Recent advances in vision-language models (VLMs) have improved Chest X-ray (CXR) interpretation in multiple aspects. However, many medical VLMs rely solely on supervised fine-tunin…
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…