CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images

TL;DR

This paper introduces CVFC, an attention-based framework for weakly supervised pathology image segmentation that generates pseudo-masks by enforcing cross-view feature consistency, reducing the need for detailed annotations.

Contribution

The paper proposes a novel multi-branch attention mechanism framework (CVFC) for weakly supervised segmentation of pathology images, improving accuracy with feature consistency constraints.

Findings

Achieved IoU of 0.7122 on WSSS4LUAD dataset

Outperformed existing methods like HistoSegNet and SEAM

Effective pseudo-mask generation with attention-based feature refinement

Abstract

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.