MM-Fundus-CLIP: Innovative Practice of a Multimodal Fundus Image Foundation Model

Title: MM-Fundus-CLIP: Innovative Practice of a Multimodal Fundus Image Foundation Model Abstract: Combining the CLIP architecture with medical imaging domain knowledge, MM-Fundus-CLIP provides a new AI solution for fundus disease diagnosis Keywords: CLIP, fundus image, multimodal learning, medical AI, contrastive learning, ophthalmology, deep learning, computer vision Original Author: Myeongkyun Kang Source: GitHub Release Date: June 5, 2026 Core Innovation: Drawing on CLIP's contrastive learning technology and introducing a multimodal fusion mechanism, it solves the problem of limited generalization ability of traditional AI models and provides a new path for fundus disease diagnosis.

Project Background and Significance

Fundus examination is an important method for ophthalmic disease diagnosis. Early signs of various diseases can be detected by observing structures such as the retina, optic nerve, and blood vessels, but high-quality analysis relies on the experience of professional physicians, making it difficult to access in areas with uneven medical resources. In recent years, medical AI has shown great potential in the field of image analysis, but most models are trained for specific tasks and have limited generalization ability. The MM-Fundus-CLIP project draws on the successful experience of CLIP and introduces large-scale language models and contrastive learning technology into the field of fundus image analysis to solve the above problems.

Technical Architecture and Training Methods

Core Architecture

Based on the OpenCLIP framework, it adopts the contrastive learning paradigm and learns the association between images and semantics through paired fundus images and text descriptions.

Multimodal Fusion Mechanism

Supports joint learning of multiple imaging modalities:

• Ultra-Widefield Fundus Imaging (UWF): Provides a wider field of view
• Optical Coherence Tomography (OCT): Provides cross-sectional structure of the retina
• Fluorescein Angiography (FA): Shows blood vessel perfusion and leakage

Training Strategies

• Data Augmentation: Enable additional augmentation via the extra-aug parameter
• Learning Rate Scheduling: Adopt a learning rate of 1e-5
• Regularly save checkpoints and retain the optimal model
• Regular zero-shot evaluation during training to monitor semantic understanding ability

Application Scenarios and Clinical Value

Zero-Shot Disease Recognition

Using CLIP's semantic alignment capability, it can identify new disease types (e.g., "diabetic retinopathy") through natural language descriptions without specific disease annotation data.

Cross-Dataset Generalization

Large-scale contrastive pre-training learns general visual-semantic representations, adapting to fundus images collected from different devices and populations.

Auxiliary Diagnosis Decision-Making

As an intelligent assistant, it quickly marks suspicious cases, prioritizes high-risk patients, and improves the efficiency of large-scale screening.

Technical Implementation Details

The code structure includes:

• open_clip: Core model implementation (modified CLIP architecture)
• open_clip_train: Training scripts and tools (supports distributed training)
• main_clip_zero.py: Zero-shot inference example Training can be configured via command-line parameters, supports single/multi-GPU training, and is open-sourced under the MIT license.

Limitations and Future Outlook

Limitations

• Data Scale: Public training datasets are relatively limited
• Clinical Validation: Need to be validated in more real clinical scenarios
• Interpretability: The black-box nature of CLIP makes the decision process difficult to explain

Future Directions

We look forward to the release of more high-quality multimodal fundus datasets, continuous optimization of the model architecture, and becoming an important infrastructure for ophthalmic AI.

Summary

MM-Fundus-CLIP represents an important direction of medical AI—applying general multimodal learning technology to professional medical image analysis. Combining the CLIP contrastive learning framework with fundus medical knowledge, it provides a new path for automatic recognition and screening of ophthalmic diseases, and is an open-source project worth attention for medical AI researchers and ophthalmic clinical developers.