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

• URL: http://arxiv.org/abs/2308.10449v1
• Date: Mon, 21 Aug 2023 03:50:09 GMT
• Title: CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
• Authors: Liangrui Pan, Lian Wang, Zhichao Feng, Liwen Xu, Shaoliang Peng
• Abstract summary: Histopathology image segmentation is the gold standard for diagnosing cancer. Many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. We propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC.
• Score: 3.2128744424771725
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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