The DOI matches its Crossref record; 13 of the 14 references it cites were found in a registry or an index.
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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“Most works [30 -34] in medical image classi fi cation jointly adopt the CNN and transformer model but did not develop a transformer-based backbone without convolutions to capture multi-granularity information in medical images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [31]
Citations
Cites
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
UNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00181 · Cited by 3,586 · cited 4×
“However, due to their localized receptive fields, these models have a poor performance in learning the longrange information, limiting their capabilities for vision tasks [3].”
InfluentialDataset of breast ultrasound images
Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405
Automated breast ultrasound lesions detection using convolutional neural networks
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Dai, Y., Gao, Y., & Liu, F. (2021) · Diagnostics · doi:10.3390/diagnostics11081384 · Cited by 511
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
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Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
Aurelio, Y. S., de Almeida, G. M., de Castro, C. L., & Braga, A. P. (2019) · Neural Processing Letters · doi:10.1007/s11063-018-09977-1 · Cited by 285
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182
MIL-VT: Multiple Instance Learning Enhanced Vision Transformer for Fundus Image Classification
Yu, S., Ma, K., Bi, Q., Bian, C., Ning, M., He, N., Li, Y., Liu, H., & Zheng, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_5 · Cited by 180
Research works printed but not linked: 1
- dosovitskiy, 2020, An image is worth 16×16 words: Transformers for image recognition at scale, ArXiv Preprint
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Sorted by citations
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,523
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Maurício, J., Domingues, I., & Bernardino, J. (2023) · Applied Sciences · doi:10.3390/app13095521 · Cited by 617
Advances in medical image analysis with vision Transformers: A comprehensive review
Azad, R., Kazerouni, A., Heidari, M., Aghdam, E. K., Molaei, A., Jia, Y., Jose, A., Roy, R., & Merhof, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103000 · Cited by 557
Takahashi, S., Sakaguchi, Y., Kouno, N., Takasawa, K., Ishizu, K., Akagi, Y., Aoyama, R., Teraya, N., Bolatkan, A., Shinkai, N., et al. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02105-8 · Cited by 440
Metadata
- Authors
- Gheflati, B., & Rivaz, H.
- Affiliations
- Concordia University (CA)
- 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)
- 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
- 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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