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---
license: cc-by-4.0
tags:
- medical
- vision-language
- visual-grounding
- multi-modal
- pre-trained
---
# Model Card: Med-GLIP
## Model Details
* **Model Name:** Med-GLIP
* **Paper Title:** Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
* **Authors:** Ziye Deng, Ruihan He, Jiaxiang Liu, Yuan Wang, Zijie Meng, Songtao Jiang, Yong Xie, Zuozhu Liu
* **Affiliations:** Zhejiang University
* **Version:** v1
* **Date:** (Presumed August 2025)
* **Model Type:** Medical Language-Image Pre-training Model with Visual Grounding capabilities.
* **Relevant Links:**
* arXiv Page: [https://arxiv.org/abs/2508.10528v1](https://arxiv.org/abs/2508.10528v1)
* DOI: [https://doi.org/10.48550/arXiv.2508.10528](https://doi.org/10.48550/arXiv.2508.10528)
* **License:** Creative Commons Attribution 4.0 International (CC BY 4.0)
* **Citation:**
```bibtex
@misc{deng2025medglip,
title={Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset},
author={Ziye Deng and Ruihan He and Jiaxiang Liu and Yuan Wang and Zijie Meng and Songtao Jiang and Yong Xie and Zuozhu Liu},
year={2025},
eprint={2508.10528},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Model Description
Med-GLIP is a medical-domain language-image pre-training model designed to enhance the understanding of fine-grained correspondences between medical images and text. In contrast to existing medical multi-modal models (e.g., MedKLIP, LLaVA-Med), Med-GLIP specifically emphasizes **visual grounding**, the ability to localize medical entities or findings mentioned in text to their corresponding regions in the image. The model's development is coupled with a large-scale, grounded medical language-image dataset, **Med-GLIP-5M**.
The model aims to overcome the limitations of existing methods in **fine-grained understanding and localization**, which is crucial for applications that require precise linking between report findings and image regions. By pre-training on the large-scale grounding dataset, Med-GLIP learns stronger cross-modal alignment capabilities.
## Intended Use
* **Primary Intended Uses:**
* Medical Visual Question Answering (VQA)
* Medical Report Generation (MRG)
* Phrase Grounding: Localizing text phrases (e.g., diseases, anatomical structures) to image regions.
* Serving as a foundational pre-trained model for various downstream medical multi-modal tasks (e.g., interactive segmentation, diagnostic assistance).
* **Primary Intended Users:**
* Medical AI researchers
* Engineers developing medical image analysis and reporting tools
* Researchers interested in multi-modal learning and visual grounding
* **Out-of-Scope Uses:**
* Direct use in clinical diagnostic decision-making without rigorous validation and regulatory approval.
* Use in non-medical image-text tasks.
## Training Data
* **Dataset:** **Med-GLIP-5M**
* A custom-built, large-scale medical language-image dataset specifically created for Med-GLIP, featuring extensive **grounding annotations** (correspondences between image regions and text phrases).
* **Dataset Construction:** The paper details the pipeline, including Data Source Analysis, Data Collection, Data Preprocessing, Quality Control, and the generation of grounding annotations (possibly utilizing tools like SAM).
* **Composition:** (Specific details depend on the full paper) Expected to include various medical imaging modalities (e.g., X-rays, CTs, MRIs) paired with corresponding radiological reports or descriptive texts, with a focus on high-quality phrase-region bounding box annotations.
## Model Architecture
* Med-GLIP is based on the architectural principles of **GLIP (Grounded Language-Image Pre-training)**, adapted for the medical domain. Key components are expected to include:
* **Image Encoder:** Likely based on a Transformer architecture (e.g., ViT or Swin Transformer) for feature extraction.
* **Text Encoder:** Likely based on a BERT variant for encoding text inputs (reports and query phrases).
* **Cross-Modal Fusion Module:** For deep interaction between image and text features.
* **Grounding Head:** To predict bounding boxes corresponding to the input text phrases based on the fused features.
* **Training Objectives:**
* **Grounding Loss:** Minimizing the difference between predicted and ground-truth bounding boxes (e.g., using L1 and GIoU loss).
* **Image-Text Contrastive (ITC) Loss:** Ensuring that matched image-text pairs are aligned in the feature space, facilitating global alignment. The formula is likely similar to: $L_{ITC} = -\log \frac{\exp(\text{sim}(I, T)/\tau)}{\sum \exp(\text{sim}(I, T')/\tau)} - \log \frac{\exp(\text{sim}(I, T)/\tau)}{\sum \exp(\text{sim}(I', T)/\tau)}$.
## Evaluation
* **Evaluation Tasks:**
* **Phrase Grounding:** Evaluated on dedicated medical grounding datasets.
* **Visual Question Answering (VQA):** Evaluated on standard medical VQA datasets (e.g., VQA-RAD, SLAKE, PathVQA).
* **Medical Report Generation (MRG):** Evaluated on datasets like MIMIC-CXR for report quality.
* **Metrics:**
* Grounding: IoU (Intersection over Union), Recall@k.
* VQA: Accuracy, AUC.
* MRG: Text generation metrics such as BLEU, ROUGE, and CIDEr.
* **Results:** The paper reports significant performance gains over previous state-of-the-art methods in several downstream tasks, particularly those requiring strong grounding capabilities. Figures and tables (e.g., Figure 7, Table 6) provide qualitative and quantitative comparisons.
## Limitations
* Model performance is highly dependent on the quality and coverage of the Med-GLIP-5M dataset.
* The model's ability to generalize to rare diseases or unseen imaging modalities/styles may be limited.
* Noise or inaccuracies introduced during the automated grounding annotation process could affect the model's precision.
* The model's computational requirements may be high for training and inference.
* (Refer to the full paper for a comprehensive discussion of limitations.)
## Bias, Risks, and Ethical Considerations
* **Data Bias:** The Med-GLIP-5M dataset may contain demographic biases (e.g., in age, gender, race representation) from its source institutions, which can be reflected in the model's performance on underrepresented groups.
* **Clinical Risk:** The model is an AI research tool and **must not** be used for primary clinical diagnosis or patient care without explicit, strict clinical validation and regulatory approval. Misinterpretation of results could lead to patient harm.
* **Interpretability:** While the grounding feature aids in interpretability, the overall decision-making process is complex, and failures should be treated with caution.
* (Refer to the full paper for a detailed discussion of ethical and societal implications.)
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