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Histopathology image segmentation is the gold standard for diagnosing cancer,and can indicate cancer prognosis. However, histopathology image segmentationrequires high-quality masks, so many studies now use imagelevel labels toachieve pixel-level segmentation to reduce the need for fine-grainedannotation. To solve this problem, we propose an attention-based cross-viewfeature consistency end-to-end pseudo-mask generation framework named CVFCbased on the attention mechanism. Specifically, CVFC is a three-branch jointframework composed of two Resnet38 and one Resnet50, and the independent branchmulti-scale integrated feature map to generate a class activation map (CAM); ineach branch, through down-sampling and The expansion method adjusts the size ofthe CAM; the middle branch projects the feature matrix to the query and keyfeature spaces, and generates a feature space perception matrix through theconnection layer and inner product to adjust and refine the CAM of each branch;finally, through the feature consistency loss and feature cross loss tooptimize the parameters of CVFC in co-training mode. After a large number ofexperiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on theWSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, andOEEM, respectively.

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