Abstract: To study the different cell components of mycosis fungoides, a multi branch squeeze and excitation network model is constructed based on 77 whole slide images of early and middle stage mycosis fungoides, and the classification of lymphocytes and epithelial cells of mycosis fungoides is realized. The network is divided into two stages:encoding and decoding. The encoding stage corresponds to one branch, and the decoding stage has three branches, corresponding to one main task and two auxiliary tasks. The main task branch outputs the results of cell classification, the auxiliary branch I outputs the cells and background, and the auxiliary branch II outputs the horizontal and vertical boundary map. In the training stage, 576 image blocks were selected from the slices and marked by professional pathologists, including 464 for training and 112 for verification. Finally, they are tested on the whole slide images. The cell segmentation accuracy and F1 score of the model are 0.943 and 0.728, respectively. The average accuracy of classification is 0.943. The experimental results show that the proposed model can recognize and classify lymphocytes and epithelial cells in mycosis fungoides, which lays an important foundation for computer-aided diagnosis of cutaneous mycosis fungoides.

Key words: mycosis fungoides, multi branch squeeze and excitation network, lymphocytes cells, horizontal and vertical map