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MedP-CLIP: Medical CLIP with region-aware prompt integration.

Jiahui Peng1, He Yao1, Jingwen Li2

  • 1Sichuan University, Chengdu, 610065, China.

Medical Image Analysis
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

We introduce MedP-CLIP, a region-aware medical vision-language model (VLM) that enhances fine-grained medical image analysis. This model excels at understanding specific regions of interest, improving zero-shot recognition and segmentation tasks.

Keywords:
Contrastive language–image pre-trainingLarge-scale datasetMedical vision–language modelRegion-aware

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Contrastive Language-Image Pre-training (CLIP) excels at global image understanding but struggles with fine-grained medical image analysis.
  • Accurate comprehension of region-of-interest (RoI) information is critical for medical image interpretation.

Purpose of the Study:

  • To develop a region-aware medical vision-language model (VLM) for enhanced fine-grained medical image analysis.
  • To integrate medical prior knowledge and a feature-level region prompt mechanism for flexible RoI comprehension.

Main Methods:

  • Proposed MedP-CLIP, a novel region-aware medical VLM.
  • Developed a feature-level region prompt integration mechanism for handling diverse prompt forms (points, boxes, masks).
  • Pre-trained on a large-scale dataset of 6.4M+ medical images and 97.3M+ region-level annotations.

Main Results:

  • MedP-CLIP demonstrated superior performance in zero-shot recognition and interactive segmentation tasks.
  • Achieved significant improvements over baseline methods in various medical AI applications.
  • Showcased cross-disease and cross-modality fine-grained spatial semantic understanding.

Conclusions:

  • MedP-CLIP offers a scalable, plug-and-play visual backbone for medical AI.
  • The model effectively combines holistic image understanding with precise regional analysis.
  • MedP-CLIP enhances the capabilities of multimodal large language models in the medical domain.