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Summary

This study introduces a graph attention network and a global classified max pooling function to improve weakly supervised pathological tissue segmentation. The novel approach enhances performance on nondominant samples and boosts overall accuracy, aiding computational pathology development.

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Area of Science:

Computational pathology
Medical image analysis
Artificial intelligence in medicine

Background:

Multilabel pathological tissue segmentation is crucial for computational pathology.
Existing weakly supervised models struggle with nondominant samples and signal aggregation.
Graph attention networks (GAT) and global classified max pooling (GCMP) offer potential solutions.

Purpose of the Study:

To enhance weakly supervised multilabel pathological tissue segmentation.
To address limitations in handling nondominant samples and signal transmission.
To improve classification accuracy in pathological image analysis.

Main Methods:

Integration of a graph attention network (GAT) module for contextual relationships and pseudo-label generation.
Development of a novel global classified max pooling (GCMP) aggregation function for effective supervision signal transmission.
Evaluation on LUAD-HistoSeg and Breast Cancer Semantic Segmentation (BCSS) datasets.

Main Results:

Improved MIoU scores for nondominant samples (necrosis and lymphocytes) in LUAD-HistoSeg by 3.3% and 3%, respectively.
Achieved an overall MIoU of 0.774 on LUAD-HistoSeg, a 1.8% increase over SOTA.
Improved MIoU scores for necrosis and lymphocytes in BCSS by 5.7% and 2%, respectively, reaching an overall MIoU of 0.721 (1.6% SOTA increase).

Conclusions:

The proposed GAT and GCMP approach effectively addresses challenges in weakly supervised segmentation.
Significant performance gains were observed, particularly for nondominant tissue types.
This work reduces manual annotation workload and advances computational pathology.
The method demonstrates a substantial improvement in state-of-the-art performance for pathological image segmentation.