A self-supervised learning model based on variational autoencoder for limited-sample mammogram classification

Abstract Deep learning models have found extensive application in medical imaging analysis, particularly in mammography classification. However, these models encounter challenges associated with limited annotated mammography public datasets. In recent years, self-supervised learning (SSL) has emerged as a noteworthy solution to addressing data scarcity by leveraging pretext and downstream tasks. Nevertheless, we recognize a notable scarcity of self-supervised learning models designed for the classification task in mammography. In this context, we propose a novel self-supervised learning model for limited-sample mammogram classification. Our proposed SSL model comprises two primary networks. The first is a pretext task network designed to learn discriminative features through mammogram reconstruction using a variational autoencoder (VAE). Subsequently, the downstream network, dedicated to the classification of mammograms, uses the encoded space extracted by the VAE as input through a simple convolutional neural network. The performance of the proposed model is assessed on public INbreast and MIAS datasets. Comparative analyzes are conducted for the proposed model against previous studies for the same classification task and dataset. The proposed SSL model demonstrates high performance with an AUC of 0.94 for density, 0.99 for malignant-nonmalignant classifications on INbreast, 0.97 for benign-malignant, 0.99 for density, and 0.99 for normal-benign-malignant classifications on MIAS. Additionally, the proposed model reduces computational costs with only 228 trainable parameters, 204.95K FLOPs, and a depth of 3 in mammogram classification. Overall, the proposed SSL model exhibits a robust network architecture characterized by repeatability, consistency, generalization ability, and transferability among datasets, providing less computational complexity than previous studies. Graphical abstract