Naotake KAMIURA

Abstract Artificial intelligence (AI)-assisted morphological analysis using whole slide images (WSIs) shows promise in supporting complex pathological diagnosis. However, the implementation in clinical settings is costly and demands extensive data storage. This study aimed to develop a compact, practical classification model using patch images selected by pathologists from representative disease areas under a microscope. To evaluate the limits of classification performance, we applied multiple pre-training strategies and convolutional neural networks (CNNs) specifically for the diagnosis of particularly challenging malignant lymphomas and their subtypes. The EfficientNet CNN, pre-trained with ImageNet, exhibited the highest classification performance among the tested models. Our model achieved notable accuracy in a four-class classification (normal lymph node and three B cell lymphoma subtypes) using only hematoxylin and eosin (H&E) stained specimens (AUC = 0.87), comparable to results from immunohistochemical and genetic analyses. This finding suggests that the proposed model enables pathologists to independently prepare image data and easily access the algorithm and enhances diagnostic reliability while significantly reducing costs and time for additional tests, offering a practical and efficient diagnostic support tool for general medical facilities.