Transformers in Medical Image Analysis: A Review
By Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen. Intelligent Medicine, 2022
401 citations recorded by the source corpus.
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.
Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/42bad1b72259aa1ff70d7ce2539220a83f1af9a4
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In Artificial Intelligence
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications (2023, background): “The described innovations have been crucial in advancing the state of the art in medical image processing, covering machine learning tasks, such as classification, segmentation, synthesis (image or video), detection, and captioning [34,45].”
- MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation (2022, methodology): “Because of its powerful feature extraction ability, researchers recently focused on how to combine ViT and its variants with UNet to obtain better results [27] [28].”
- A Review of the Role of Artificial Intelligence in Healthcare (2023, methodology): “Moreover, a deep learning model named transformer is used in medical imaging analysis and includes registration, detection, classification, image-to-image translation, segmentation, and video-based applications [34].”