CLID: A contrastive learning approach for multi-class classification of breast cancer histopathological images with imbalanced distribution

Globally, breast cancer is recognized as the leading form of malignant tumor in women, significantly impacting public health on an international scale. However, current breast cancer classification methods often overlook the imbalanced data distribution within breast cancer datasets. This leads to models being biased toward majority-class samples during classification, while underperforming on minority-class samples, thereby reducing overall classification accuracy. Moreover, multi-classification approaches can provide physicians with richer diagnostic information and better support the development of treatment strategies. To address these challenges, we introduce a breast cancer multi-classification algorithm, CLID (Contrastive Learning for Imbalanced Distribution), which utilizes contrastive learning to address the problem of imbalanced data distribution. Specifically, a preprocessed convolutional neural network is employed to extract features from histopathological images. Learnable archetype vectors are employed to model inter-category similarities, and a contrastive learning loss function is used to train the model. Experimental results on the BreakHis and BRACS datasets demonstrate that the proposed method achieves superior performance compared with existing approaches and representative imbalance-aware learning strategies, showing strong robustness under different magnifications.