Breast TransFG Plus: Transformer-based fine-grained classification model for breast cancer grading in Hematoxylin-Eosin stained pathological images

Breast cancer is one of the most common malignancies in women, and the pathological grading of breast cancer is very important for the prognosis of breast cancer. But classification of breast Hematoxylin-Eosin (HE) stained pathological images by deep learning for breast cancer grading is difficult due to morphological similarities between different grades. Therefore, it is essential to have an efficient and accurate method of breast cancer grading. In this paper, a transformer-based fine-grained classification model named Breast TransFG Plus is proposed for breast cancer grading. Targeting the widespread distribution of cells in breast HE stained pathological images, this paper proposes part selection module plus, which is a kind of key information extraction method based on matrix addition, and double head classification structure, which is a kind of dual stream network structure. And balanced sampling based on data augmentation is used in the training set. The proposed method has been evaluated on a public dataset and the classification accuracy, precision and recall on the test set are 99.39%, 99.18% and 99.59%, the number of parameters is 93 M. And it is proved that the proposed model is superior to previous studies by comparing with them. Breast TransFG Plus can achieve efficient and accurate breast cancer grading, and has the potential to meet clinical computer-aided diagnosis needs.