3 papers
cs.CV2026
CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
Sergios Gatidis, Curtis Langlotz, Christian Bluethgen
Vision-language models (VLMs) pretrained on large-scale image-text pairs demonstrate strong image-level understanding, but are primarily optimized for global alignment and do not e…
cs.CV2026
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…
cs.CV2026
Sparse Autoencoders for Interpretable Medical Image Representation Learning
Philipp Wesp, Robbie Holland, Vasiliki Sideri-Lampretsa +1
Vision foundation models (FMs) achieve state-of-the-art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot i…