Multi-task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images

Deep learning models may be useful for the differential diagnosis of breast cancer histopathology images. However, most modern deep learning methods are data-hungry. But, large annotated dataset of breast cancer histopathology images are elusive. As a result, the application of such deep learning methods for the differential diagnosis of breast cancer is limited. To deal with this problem, we propose a few-shot learning approach for the differential diagnosis of the histopathology images of breast tissue. Our model is trained through two stages. We initially train our model for a binary classification task of identifying benign and malignant tissues. Subsequently, we propose a multi-task learning strategy for the few-shot differential diagnosis of breast tissues. Experiments on publicly available breast cancer histopathology image datasets show the efficacy of the proposed method.