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Researchers at Fuzhou University in Fujian, China have developed a deep learning model fusion algorithm for the diagnosis of gastric mucosa-associated lymphoid tissue (MALT) lymphoma. The algorithm uses hematoxylin-eosin (H&E) whole slide images (WSIs) to accurately diagnose MALT lymphoma, which is a type of malignant tumor originating from the lymphohematopoietic system. The proposed framework achieved an accuracy of 98.53% using image patches and 94.96% on 258 WSIs, demonstrating its potential use in clinical practice. The research was funded by the National Natural Science Foundation of China and the Fujian Health Commission. [Extracted from the article]