面向WSI的乳腺病理亚型分类研究

为实现乳腺病理WSI图像的精准分类，提出了一种基于混合连接的门控卷积神经网络分类方法。搭建了局部残差连接和全局稠密连接的混合模块，将压缩激活门控单元嵌入混合模块，建立了混合模块与过渡层交替连接的骨干网络。结合基于四叉树分割的图像数据增强方法训练模型，基于BreastSet临床数据集的实验结果得出，该方法的图像级、患者级和病理级准确率分别达到92.24%、92.83%和92.18%，相较其他方法，其准确率提高，参数量和计算量降低，更具有临床应用价值。

Research of breast pathological subtype classification on WSI

For precise classification of pathological breast WSI images, the paper 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 show 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 has great clinical application value for improving accuracy as well as reducing number of parameters and amount of computation.