Review
Version 1
Preserved in Portico This version is not peer-reviewed
Ultrasound Image Analysis with Vision Transformers – Review
Version 1
: Received: 3 January 2024 / Approved: 4 January 2024 / Online: 4 January 2024 (03:11:09 CET)
A peer-reviewed article of this Preprint also exists.
Vafaeezadeh, M.; Behnam, H.; Gifani, P. Ultrasound Image Analysis with Vision Transformers—Review. Diagnostics 2024, 14, 542. Vafaeezadeh, M.; Behnam, H.; Gifani, P. Ultrasound Image Analysis with Vision Transformers—Review. Diagnostics 2024, 14, 542.
Abstract
Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low imaging quality and high variability. There is a critical need to develop advanced automatic US image analysis methods to enhance diagnostic accuracy and objectivity. Vision transformer, a recent innovation in machine learning, has demonstrated significant potential in various research fields, including general image analysis and computer vision, due to its capacity to process large datasets and learn complex patterns. Its suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformer and discusses its applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in its application in medical US image analysis. Vision transformer has shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and is expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.
Keywords
Transformer; Ultrasound (US); Deep Learning; Convolutional Neural Network(CNN); Vision transformer(ViT); Swin Transformer
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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