# Vision Transformer for Classification of Breast Ultrasound Images

arXiv: https://arxiv.org/abs/2110.14731 · HTML: https://mlsanity.com/paper/2110.14731/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2021.

## Built on by (most cited first, 21 of 21)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Vision Mamba for Classification of Breast Ultrasound Images](https://mlsanity.com/paper/2407.03552/) (2024)
- [Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers](https://mlsanity.com/paper/2306.06877/) (2023)
- [ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification](https://mlsanity.com/paper/2211.00749/) (2022)
- [Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network](https://mlsanity.com/paper/2308.02101/) (2023)
- [Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care](https://mlsanity.com/paper/2309.00252/) (2023)
- [T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers](https://mlsanity.com/paper/2403.04523/) (2024)
- [GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification](https://mlsanity.com/paper/2203.06184/) (2022)
- [TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction](https://mlsanity.com/paper/2301.10829/) (2023)
- [Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis](https://mlsanity.com/paper/2306.05297/) (2023)
- [Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation](https://mlsanity.com/paper/2308.14213/) (2023)
- [A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images](https://mlsanity.com/paper/2502.19217/) (2025)
- [CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classification](https://mlsanity.com/paper/2302.02314/) (2023)
- [UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis](https://mlsanity.com/paper/2401.03173/) (2024)
- [Hands-on Evaluation of Visual Transformers for Object Recognition and Detection](https://mlsanity.com/paper/2512.09579/) (2025)
- [Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound](https://mlsanity.com/paper/2603.01295/) (2026)

## Abstract (arXiv)

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 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 that are 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. The results are provided as classification accuracy and Area Under the Curve (AUC) metrics, and the performance is compared with the state-of-the-art CNNs. The results indicate that the ViT models have comparable efficiency with or even better than the CNNs in classification of US breast images.