Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.

Area of Science:

• Medical image analysis
• Computer vision
• Artificial intelligence

Background:

• Contrastive Language-Image Pre-training (CLIP) excels in visual representation and generalization.
• CLIP's application in pathology is challenged by domain shifts and limited labeled data.
• Efficient adaptation strategies are crucial for scalable pathology image analysis.

Purpose of the Study:

• Introduce Path-CLIP, a framework for rapid CLIP adaptation to pathology tasks.
• Address domain shifts and catastrophic forgetting in CLIP for medical imaging.
• Enable effective utilization of limited labeled data in pathology.

Main Methods:

• Propose Residual Feature Refinement (RFR) for integrating source and task-specific knowledge.
• Implement Hidden Representation Perturbation (HRP) and Dual-view Vision Contrastive (DVC) to prevent overfitting.
• Utilize Doublet Multimodal Contrastive Loss (DMCL) for fine-tuning CLIP on pathology datasets.

Main Results:

• Path-CLIP demonstrates effective adaptation of pre-trained CLIP for pathology tasks.
• Achieved over +19% accuracy improvement on the PCam dataset using only 0.1% labeled data.
• Required only 10 minutes of fine-tuning on a single GPU.

Conclusions:

• Path-CLIP offers a competitive and efficient solution for adapting CLIP to pathology.
• The framework successfully mitigates challenges associated with limited data and domain specificity.
• Path-CLIP facilitates scalable and accurate analysis of pathology images.