ArticleScientific reports2026
PatchCLIP enables region specific contrastive health record and image joint training with patch embedding loss.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Vision-Language (VL) models such as Contrastive Language-Image pretraining (CLIP) have shown remarkable zero-shot classification capabilities by jointly learning from large-scale image-text datasets using multimodal self-supervised learning (SSL). However, while these models capture strong global semantics, they often struggle with fine-grained spatial understanding, thereby limiting their effectiveness in downstream tasks like object detection and medical abnormality localization
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