GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations 事件

GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations arXiv:2606.03180v1 Announce Type: new Abstract: Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows. However, this pairing reveals a mismatch in scale: each finding occupies only a small region of the image, yet supervision is provided only at the global image-report level. This poses a central challenge: