Self-Distilled Supervised Contrastive Learning for diagnosis of breast cancers with histopathological images

Convolutional neural networks (CNN) and its variants have been widely used for developing the histopathological image based computer-aided diagnosis (CAD). However, the annotated data are scarce in clinical practice, and limited training samples generally cannot well train the CNN model, resulting in degraded predictive performance. To this end, we propose a novel Self-Distilled Supervised Contrastive Learning (SDSCL) algorithm to improve the diagnostic performance of a CNN-based CAD for breast cancer. In particular, the original histopathological images are first decomposed into H and E stain views, which are served as the augmented sample pairs in the supervised contrastive learning (SCL). Due to the complementary characteristic of the two stain views, this data-driven SCL guide the CNN model to efficiently learn the discriminative features, alleviating the problem of small sample size. Furthermore, self-distillation is embedded into the SCL framework, in which the CNN model jointly distills itself and conducts SCL to further improve feature representation. The proposed SDSCL is evaluated on two public breast histopathological datasets, which outperforms all the compared algorithms. Its average classification accuracy, precision, recall, and F1 scores are 94.28%, 94.64%, 94.58%, 94.34%, respectively, on Bioimaging dataset, and 80.44%, 81.92%, 80.57%, 80.10% on Databiox dataset. The experimental results on two datasets indicate that SDSCL has the potential for the histopathological image based CAD.