Vision Transformers for Classification of Breast Ultrasound Images

Abstract

Medical ultrasound (US) imaging has become a prominent modality for breast cancer imaging due to its ease of use, low cost, and safety. In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in the automatic classification of US images. Despite their success, their restricted local receptive field limits their ability to learn global context information. Recently, Vision Transformer (ViT) designs, based on self-attention between image patches, have shown great potential to be an alternative to CNNs. In this study, for the first time, we utilize ViT to classify breast US images using different augmentation strategies. We also adopted a weighted cross-entropy loss function since breast ultrasound datasets are often imbalanced. The results are provided as classification accuracy and Area Under the Curve (AUC) metrics, and the performance is compared with the SOTA CNNs. The results indicate that the ViT models have comparable efficiency with or even better than the CNNs in the classification of US breast images. Clinical relevance- This work shows the potential of Vision Transformers in the automatic classification of masses in breast ultrasound, which helps clinicians diagnose and make treatment decisions more precisely.

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