CVFC: Attention-Based Cross-View Feature Consistency for Weakly
  Supervised Semantic Segmentation of Pathology Images
Liangrui Pan Lian Wang Zhichao Feng Liwen Xu Shaoliang Peng
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.