Cited by 225AI in cancer detectionMedical Image Segmentation TechniquesBreast Lesions and Carcinomas

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Vision Transformers for Classification of Breast Ultrasound Images

Gheflati, Behnaz, Rivaz, Hassan·2022·2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

DOI 10.1109/embc48229.2022.9871809

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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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Authors: Gheflati, B., & Rivaz, H.
Affiliations: Concordia University (CA)
ORCID: Gheflati, Behnaz
Rivaz, Hassan
Venue: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publisher: IEEE
Published: 2022-07-11
Volume: 2022
Pages: 480-483
ISSN: 2375-7477 (ISSN-L)
DOI: 10.1109/embc48229.2022.9871809
OpenAlex: W4294975187
Cited by: 225
References: 14
Citations by year: 2026: 28 · 2025: 65 · 2024: 56 · 2023: 59 · 2022: 16
FWCI: 27.10
Type: conference paper
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
PubMed (publishedVersion)
Funders: Natural Sciences and Engineering Research Council of Canada
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
Keywords: vision transformer, breast ultrasound, breast cancer, mass classification, self-attention, data augmentation, class imbalance, weighted cross-entropy loss, CNN comparison, automated diagnosis
MeSH terms: Breast, Breast Neoplasms, Female, Humans, Ultrasonography, Ultrasonography, Mammary, Neural Networks, Computer

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Vision Transformers for Classification of Breast Ultrasound Images

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