Novel Meta Attention Guided Framework for Breast Abnormality Classification With Combination of FSL and DA

Due to the unavailability of sufficient amounts of breast histopathological images for medical abnormality detection especially for many rare cancer stages, the applicability of the traditional deep learning models to achieve good prediction performance is a challenging task. To address such challenges, we proposed Meta Attention Guided Few-Shot Learning (MAG-FSL) for a robust and highly effective model for Domain Adaptive Few-Shot Learning (DA-FSL) problem in breast abnormality classification using histopathological images. MAG-FSL is investigated on two publicly accessible breast cancer databases i.e., BreakHis and BreastCancer-IDC-Grades. Experimental results reveal that our proposed MAG-FSL significantly outperformed the state-of-the-art Few-Shot Learning (FSL) and DA-FSL methods for breast histopathological image classification in the single domain and the shifted domain FSL problems. For the single domain FSL problem, we achieved an average accuracy of 92.42%. For the DA-FSL problem, we achieved average accuracies of 76.33% and 69.95% on BreakHis and BreastCancer-IDC-Grades databases, respectively.