AI system for diagnosing mucosa-associated lymphoid tissue lymphoma and diffuse large B cell lymphoma using ImageNet and hematoxylin and eosin-stained specimens.

ABSTRACT: 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 pretraining strategies and convolutional neural networks (CNNs) specifically for the diagnosis of particularly challenging malignant lymphomas and their subtypes. The EfficientNet CNN, pretrained with ImageNet, exhibited the highest classification performance among the tested models. Our model achieved notable accuracy in a four-class

SUBMITTER: Yamaguchi S 

PROVIDER: S-EPMC12069809 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature