TransBreast-Net: An interpretable vision transformer ensemble for breast cancer histopathology classification

Histopathology classification of breast cancer is still a case of difficulty in sorting out the complex morphology of the tissues, low inter-class variability, and manual biopsy analysis which is time consuming, costly, and inter-observer-dependent. Convolutional neural network-based methods have been shown to perform well for breast cancer imaging diagnosis, but they frequently lack the ability to capture the long-range spatial dependencies and lack interpretability for clinical decision-making. To tackle these problems, this study introduces an interpretable vision transformer ensemble framework TransBreast-Net for breast cancer histopathology classification. The proposed framework is based on transfer learning from large data augmentation and strong preprocessing on BreakHis and ICIAR datasets. The architectures of three transformers, namely CaiT S24 224, DeiT Small Patch16_224, and Swin Small Patch4_ Window7_224, are used to extract the complementary representations of local and global features. However, ensemble methods with Swin + DeiT for binary classification, and Swin + CaiT for multi-class classification are designed to enhance classification robustness and generalization. Experimental results show good performance with 99.35% accuracy in binary classification (benign vs. malignant) and 97% accuracy in multi-class classification (benign, in situ, invasive, and normal). In addition, visual explanations are embedded using Gradient-weighted Class Activation Mapping to enhance interpretability by identifying diagnostically relevant tissue regions that are important in the model decision-making process. The proposed TransBreast-Net framework exhibits high classification accuracy, good robustness, and has potential clinical applications for artificial intelligence diagnosis in breast cancer.