Enhancing Weakly Supervised Semantic Segmentation With Multi-Label Contrastive Learning and LLM Features Guidance.

Area of Science:

• Digital Pathology
• Computer Vision
• Medical Image Analysis

Background:

• Histopathological whole-slide image (WSI) segmentation is crucial for medical diagnostics.
• Traditional segmentation methods demand extensive pixel-level annotations, which are time-consuming.
• Weakly supervised semantic segmentation (WSSS) offers a solution by utilizing less-intensive patch-level labels.

Purpose of the Study:

• To develop an effective WSSS method for complex WSIs with multi-label characteristics.
• To overcome limitations of single-label contrastive learning approaches in WSI analysis.
• To reduce annotation burden while improving segmentation precision in histopathology.

Main Methods:

• A novel multi-label contrastive learning framework for WSSS.
• Incorporation of class-specific embeddings derived from classifier weights.
• Utilizing Large Language Model (LLM) features for attention-based semantic enrichment.
• A Robust Learning approach to mitigate noisy pseudo-labels using multi-layer features.

Main Results:

• Demonstrated superior performance on histopathological image segmentation tasks.
• Achieved leading results on the LUAD and BCSS datasets.
• Effectively addressed challenges posed by WSI complexity and sparse labels.

Conclusions:

• The proposed multi-label contrastive learning method significantly enhances WSSS for WSIs.
• LLM feature guidance and robust learning strategies improve segmentation accuracy and reliability.
• This approach offers a more efficient and effective solution for histopathological image analysis.