Virtual Adversarial Training for Semi-supervised Breast Mass Classification

TL;DR

This paper introduces a semi-supervised deep learning approach using virtual adversarial training to improve breast mass classification in mammograms, effectively leveraging unlabeled data to enhance accuracy.

Contribution

It proposes a novel VAT-based semi-supervised method for mammogram classification, demonstrating improved performance with limited labeled data.

Findings

VAT-based models outperform supervised-only models.

High accuracy achieved with 40% and 80% labeled data.

Effective utilization of unlabeled data improves classification.

Abstract

This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acquire in practice. To overcome this limitation, we propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to leverage and learn useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss aims at enhancing model robustness against virtual adversarial perturbation, thus improving model generalizability. To evaluate the performance of our VAT-based CAD scheme, we retrospectively assembled a total of 1024 breast mass images, with equal number of benign and malignant masses. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740 and 0.760, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images.