AI RESEARCH
GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations
arXiv CS.LG
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ArXi:2606.03180v1 Announce Type: cross 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: prior approaches spread weight densely across all patches rather than concentrating on the sparse subset relevant to a given query.