A deep learning model fusion algorithm for the diagnosis of gastric Mucosa-associated lymphoid tissue lymphoma

Lymphoma is a malignant tumor originating from the lymphohematopoietic system. At present, pathological evaluation is one of the important methods to diagnose malignant lymphoma. In clinical practice, the diagnosis of lymphoma, especially in newly diagnosed patients, depends mainly on histopathological examination of the lesion. The type of lymphoma is determined by repeatedly comparing hematoxylin-eosin (H&E) whole slide images (WSIs) and immunohistochemical WSIs under a microscope. It is a repetitive, tedious, and time-consuming process. Therefore, it is extremely important to establish a highly accurate and standardized lymphoma diagnosis algorithm. In this paper, we developed an innovative deep-learning framework based on multi-model fusion, which only uses the H&E slides, with special attention to gastric Mucosa-associated lymphoid tissue (MALT) lymphoma diagnosis. The proposed framework can evaluate and improve the auxiliary ability of the convolutional neural network (CNN) in clinical practice for the diagnosis of gastric MALT lymphoma. The proposed method achieved an accuracy of 98.53% using image patches and an accuracy of 94.96% on 258 WSIs. These results show the high accuracy in the diagnosis of MALT lymphoma and its potential use in clinical practice. In addition, we also estimated the 95% confidence interval of WSIs prediction values. The result shows that the proposed framework has a high degree of differentiation in the interpretation between gastric MALT lymphoma and normal pathological tissues.