Cross-platform multi-cancer histopathology classification using local-window vision transformers.

ABSTRACT: Cancer remains one of the leading causes of global mortality, with lung, colon, skin, and breast cancers contributing significantly to the disease burden. Accurate and timely classification of histopathological images is critical for effective diagnosis and treatment planning. However, existing deep learning models for histopathology often achieve strong results but remain limited to single-cancer classification, lack generalizability across datasets, and provide little transparency for clinical use. To address these gaps, we propose CancerDet-Net, a comprehensive unified framework capable of classifying nine histopathological subtypes across four major cancer types. CancerDet-Net integrates separable convolutional layers, Vision Transformer (ViT) blocks with local-window sparse self-atten

SUBMITTER: Nayeem MD 

PROVIDER: S-EPMC12630626 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature