CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
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 explicitly encode fine-grained anatomical structure, limiting their suitability for spatially precise tasks such as segmentation. We introduce CheXanatomy, a framework that integrates explicit anatomical knowledge into a pretrained VLM through autoregressive token-space supervision. Instead of adding task-specific decoder heads, the model is trained to generate anatomical segmentation masks via next-token prediction. To enable scalable supervision, we synthesize realistic chest radiographs from CT volumes and for...
| Authors | Sergios Gatidis, Curtis Langlotz, Christian Bluethgen |
|---|---|
| Primary category | cs.CV |
| Published | 7 June 2026 |
| Last updated at source | 25 June 2026 |
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