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arxiv: 2604.11197 · v1 · submitted 2026-04-13 · 💻 cs.CV

MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration

Jiahui Peng , He Yao , Jingwen Li , Yanzhou Su , Sibo Ju , Yujie Lu , Jin Ye , Hongchun Lu
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Xue Li Lincheng Jiang Min Zhu Junlong Cheng
This is my paper

Pith reviewed 2026-05-10 15:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords medical vision-language modelregion-aware prompt integrationzero-shot recognitioninteractive segmentationmedical image pre-trainingCLIP adaptationmultimodal large language models
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The pith

MedP-CLIP adds a feature-level region prompt mechanism to CLIP for medical images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates MedP-CLIP, a vision-language model that processes both entire medical images and specific regions such as lesions or organs when given prompts. It builds this by pre-training on a dataset of over 6.4 million images that include 97.3 million region-level annotations and by inserting prompt information directly into the image features. This design lets the model accept inputs like points, bounding boxes or masks from clinicians or other systems. If the approach works, the same backbone can support zero-shot disease identification, user-guided segmentation, and improved visual features inside larger medical language models.

Core claim

MedP-CLIP is a region-aware medical vision-language model that incorporates medical prior knowledge via a feature-level region prompt integration mechanism. Pre-trained on more than 6.4 million medical images with 97.3 million region annotations, the model responds flexibly to prompt forms such as points, bounding boxes and masks while retaining global contextual awareness when attending to local regions. Experiments show it exceeds baseline methods on zero-shot recognition, interactive segmentation, and when used to strengthen multimodal large language models, offering a scalable plug-and-play visual backbone for medical AI.

What carries the argument

feature-level region prompt integration mechanism, which merges prompt information into the vision features to enable region-specific focus without discarding overall image context.

If this is right

  • Outperforms standard methods on zero-shot recognition across diseases and imaging modalities.
  • Enables interactive segmentation guided by user-provided points, boxes or masks.
  • Acts as a plug-and-play visual backbone that raises performance of multimodal large language models on medical tasks.
  • Delivers both holistic image understanding and precise regional analysis in one model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The prompt mechanism could transfer to other image domains that need local detail, such as remote sensing or manufacturing inspection, if the integration step proves domain-agnostic.
  • Combining the model with automated prompt generators from diagnostic software might allow real-time highlighting of suspicious regions during scans.
  • Gains may depend heavily on the size of the annotated dataset, so similar scaling of region labels could benefit other vision-language models even without the new integration step.

Load-bearing premise

The feature-level integration of region prompts lets the model answer different prompt types while still keeping awareness of the full image context.

What would settle it

Test whether accuracy on global image description tasks drops when a small region prompt is supplied versus the unprompted model; equal or better global performance would support the claim.

Figures

Figures reproduced from arXiv: 2604.11197 by He Yao, Hongchun Lu, Jiahui Peng, Jingwen Li, Jin Ye, Junlong Cheng, Lincheng Jiang, Min Zhu, Sibo Ju, Xue Li, Yanzhou Su, Yujie Lu.

Figure 1
Figure 1. Figure 1: MedP-CLIP can be flexibly deployed for various downstream tasks, significantly enhancing the region-aware analysis capability of medical imaging. This model supports direct application and plug-and-play integration, and accepts explicit prompts from users as well as outputs from perceptual models. masking neglects potentially informative surrounding structures and both undermine the holistic reasoning esse… view at source ↗
Figure 2
Figure 2. Figure 2: MedP-CLIP: data, architecture and training. From IMed-361M we convert pixel-level organ/lesion masks into region–text pairs by generating prompts (point/box/mask) and GPT-4o based clinical descriptions (a). Dataset statistics across modalities, anatomies and mask ratios (b). Model pipeline: the image is encoded by a ViT, while the prompt is embedded by a lightweight Prompt Encoder (c). A feature-level Atte… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of attention maps for zero-shot classification on COVID-CT and ACL datasets. average accuracy of 54.35% across all datasets, representing a 1.72% improvement over the current top-performing GenMedCLIP baseline. To further corroborate these quantitative findings, we provide qualitative visual comparisons in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison of region-level classification on multi-organ datasets. “Δ” denotes the performance difference between Top-5 and Top-1. “Rect.” indicates rectangular region prompts generated from original masks via Alpha-CLIP. We extended the evaluation to multi-organ matching tasks on AMOS2022 and 3D-IRCADB. Due to the lack of region-level tuning in medical baseline models, we benchmarked against A… view at source ↗
Figure 5
Figure 5. Figure 5: Visual validation of MedP-CLIP’s region-aware retrieval capabilities [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results of MedP-CLIP. Prompt guides the visual encoder to focus on the RoIs. the IMIS-Net (Cheng et al., 2025) protocol, we fine-tuned the decoder (<1% of encoder parameters) for 5 epochs on IMed-361M, achieving rapid convergence. As detailed in Tab. 5, MedP-CLIP+SAM delivers consistent and substantial improvements over the original SAM across all datasets. Critically, our model achieves new st… view at source ↗
Figure 7
Figure 7. Figure 7: Segmentation results of MedP-CLIP+SAM with five points interaction. In addition, we provide comprehensive qualitative comparisons in [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative experimental comparison of interactive segmentation. results explicitly corroborate our quantitative findings, proving that our feature-level region integration effectively mitigates error propagation in complex clinical scenarios [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison of region-level medical Visual Question Answering (VQA) capabilities. J. Peng et al.: Preprint submitted to Elsevier Page 12 of 20 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Exploration of model prompt sensitivity and boundary conditions. By comparing single point, multiple points, and bounding box interactions, this figure reveals the limitations of the model when dealing with out-of-distribution noise (left column) and ambiguous local features (middle column), using the right column as a baseline success reference. Case1: Impact of Artifacts and Noise The first column of [… view at source ↗
Figure 11
Figure 11. Figure 11: The relationship between MedP-CLIP pre-training data scaling and zero-shot classification. 5.2. Encoder Unfreezing Optimizing MedP-CLIP performance, we trained the model on the TotalSegmentator dataset (Wasserthal et al., 2023) while unfreezing varying numbers of vision encoder layers. Experiments employed the ViT-B/16 architec￾ture (Radford et al., 2021) (containing 12 Transformer blocks). Model performa… view at source ↗
Figure 12
Figure 12. Figure 12: t-SNE visualization of semantic embeddings from our synthesized clinical descriptions (red circles) and human￾annotated RadGenome-Chest CT descriptions (green triangles) across 13 anatomical categories. (4) Semantic Feature Distribution Validation To provide intuitive empirical support from the feature space perspective, we conducted a quantitative analysis of the semantic and linguistic distribution of t… view at source ↗
read the original abstract

Contrastive Language-Image Pre-training (CLIP) has demonstrated outstanding performance in global image understanding and zero-shot transfer through large-scale text-image alignment. However, the core of medical image analysis often lies in the fine-grained understanding of specific anatomical structures or lesion regions. Therefore, precisely comprehending region-of-interest (RoI) information provided by medical professionals or perception models becomes crucial. To address this need, we propose MedP-CLIP, a region-aware medical vision-language model (VLM). MedP-CLIP innovatively integrates medical prior knowledge and designs a feature-level region prompt integration mechanism, enabling it to flexibly respond to various prompt forms (e.g., points, bounding boxes, masks) while maintaining global contextual awareness when focusing on local regions. We pre-train the model on a meticulously constructed large-scale dataset (containing over 6.4 million medical images and 97.3 million region-level annotations), equipping it with cross-disease and cross-modality fine-grained spatial semantic understanding capabilities. Experiments demonstrate that MedP-CLIP significantly outperforms baseline methods in various medical tasks, including zero-shot recognition, interactive segmentation, and empowering multimodal large language models. This model provides a scalable, plug-and-play visual backbone for medical AI, combining holistic image understanding with precise regional analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes MedP-CLIP, a region-aware medical vision-language model extending CLIP via a feature-level region prompt integration mechanism. This allows flexible handling of prompts (points, bounding boxes, masks) while preserving global context. The model is pretrained on a large dataset of over 6.4 million medical images and 97.3 million region-level annotations, and is evaluated on zero-shot recognition, interactive segmentation, and as a backbone for multimodal large language models, with claims of significant outperformance over baselines.

Significance. If the reported results and ablations hold, the work supplies a scalable, plug-and-play visual backbone for medical AI that combines holistic image understanding with precise regional analysis. The scale of the pretraining corpus and the prompt-flexible architecture constitute concrete strengths that could support downstream clinical applications.

minor comments (3)
  1. Abstract: the claim of 'significantly outperforms baseline methods' would be more informative if accompanied by at least one or two key quantitative metrics (e.g., accuracy deltas or mIoU improvements) rather than remaining purely qualitative.
  2. [§3] §3 (Dataset and Pretraining): while the dataset size is stated, additional detail on annotation protocol, quality filtering, and cross-modality balancing would aid reproducibility and allow readers to assess potential biases.
  3. [Figure 3] Figure 3 (architecture diagram): the feature-level fusion block would benefit from explicit arrows or labels indicating how prompt embeddings are injected into the visual features at each stage.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation for minor revision. We appreciate the recognition of the scalable pretraining corpus and prompt-flexible architecture as strengths.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical model construction (MedP-CLIP) with a specified feature-level region prompt integration architecture, pre-trained on an external large-scale dataset of 6.4M images and 97.3M annotations, followed by experimental validation on zero-shot, segmentation, and MLLM tasks. No equations, first-principles derivations, fitted-parameter predictions, or load-bearing self-citation chains appear in the provided text; the central claims rest on the described mechanism and reported empirical outperformance rather than any reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the unverified effectiveness of the region prompt integration and the quality of the 6.4M-image dataset; no free parameters or invented physical entities are stated.

axioms (1)
  • domain assumption CLIP-style contrastive pre-training on image-text pairs produces useful global representations that can be extended to regional prompts.
    The paper builds directly on CLIP's established global alignment success.
invented entities (1)
  • feature-level region prompt integration mechanism no independent evidence
    purpose: To allow flexible handling of point, box, and mask prompts while preserving global context.
    This is the core novel component introduced by the paper.

pith-pipeline@v0.9.0 · 5563 in / 1294 out tokens · 46946 ms · 2026-05-10T15:50:56.118485+00:00 · methodology

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Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

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    work page doi:10.1016/j.knosys.2025.113538 2018

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    Scientific data 9, 762

    Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific data 9, 762. Hu, K., Xiao, Y., Zhang, Y., Gao, X., 2024. Multi-view masked contrastive representation learning for endoscopic video analysis. Advances in Neural Information Processing Systems . Huang, X., Shen, L., Liu, J., Shang, F., Li, H., Huang, H., Ya...

    work page arXiv 2022