Breast cancer is the most common cancer in women. The single neural network used in breast cancer path-ological image classification has the following defects:the convolutional neural network (CNN) lacks the ability to extract global context information while the Transformer lacks the ability to depict local lesion details. To alleviate the problem,a novel model,named multi-view Transformer coding and online fusion mutual learning (MVT-OFML),is proposed for breast cancer pathological image classification. First,ResNet-50 is employed to extract local features in images. Then,a new multi-view Transformer (MVT) coding module is designed to capture the global context information. Finally,a novel online fusion mutual learning (OFML) framework based on the Logits and middle feature layers is designed to implement the bi-directional knowledge transfer between ResNet-50 and the MVT coding module. This makes the two networks com-plement each other to complete breast cancer pathological image classification. Experiments validated on BreakHis and BACH show that compared to the best baseline,the performance improvements of accuracy are 0.90％ and 2.26％,respec-tively,whereas the corresponding improvements of average F1 score are 4.75％ and 3.21％,respectively.
关键词
乳腺癌/病理图像分类/多视角Transformer/卷积神经网络/在线融合互学习
Key words
breast cancer/pathological image classification/multi-view Transformer/convolution neural network/online fusion mutual learning
维普
万方数据