面向WSI的乳腺病理亚型分类研究 postprint

Abstract: For precise classification of pathological breast WSI images, the study proposed the gated convolution network based on hybrid connection (HC-GCN) , set up a hybrid block of local residual connection and global dense connection, and through embedding the Squeeze -Excitation-and-Gated (SEG) module into the hybrid block, established a backbone network for alternate connection between the hybrid block and the transition layer. In combination with a training model for image data enhancement based on quad-tree image segmentation method, the experimental results based on the BreastSet clinical data set showed 92.24%, 92.83% and 92.18% of accuracy at the image level, patient level and pathology level respectively. Therefore, compared with other methods, this method is of great clinical application value for improved accuracy as well as reduced number of parameters and amount of computation.