CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding

2026-05-13 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, medium

Summary

CA-GCL, a novel Cross-Anatomy Global-Local Contrastive Learning framework, addresses representation collapse in Fine-grained Vision-Language Pre-training (FVLP) for 3D medical image understanding. Existing FVLP models often suffer from text embeddings of distinct anatomical structures becoming highly clustered, leading to hypersensitivity to prompt variations. CA-GCL introduces a global contrastive objective to separate anatomical categories in the latent space, counteracting the aggregation caused by local alignment. It also incorporates a clinical-aware text augmentation strategy using permutation invariance and partial completeness to improve robustness against descriptive incompleteness. Evaluations on the CT-RATE and Rad-ChestCT datasets show CA-GCL outperforms other VLP paradigms in zero-shot abnormality detection, demonstrating superior performance, strong cross-dataset generalization, and reduced performance variance across diverse prompt templates by transforming the collapsed textual similarity distribution into a bell-shaped one.

Key takeaway

For Computer Vision Engineers developing 3D medical image understanding systems, CA-GCL offers a robust framework to mitigate representation collapse and prompt sensitivity. You should consider integrating its global-local contrastive learning and clinical-aware text augmentation strategies to enhance model performance, improve cross-dataset generalization, and ensure more reliable clinical deployment, particularly for zero-shot abnormality detection tasks.

Key insights

CA-GCL uses global-local contrastive learning and clinical-aware text augmentation to prevent representation collapse in 3D medical FVLP.

Principles

• Enforce separation between anatomical categories.
• Counteract local alignment aggregation with global contrast.
• Enhance robustness via clinical-aware text augmentation.

Method

CA-GCL applies a global contrastive objective for anatomical separation and a clinical-aware text augmentation strategy based on permutation invariance and partial completeness.

In practice

• Improve zero-shot abnormality detection in 3D medical images.
• Reduce prompt sensitivity in clinical deployment.
• Achieve cross-dataset generalization for VLP models.

Topics

• Cross-Anatomy Contrastive Learning
• 3D Medical Image Understanding
• Vision-Language Pre-training
• Representation Collapse
• Zero-shot Abnormality Detection

Code references

• krishnabits001/domain_specific_cl
• aimagelab/CoDE

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.