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用于CT图像去噪和重建的自监督学习:综述

Self-supervised learning for CT image denoising and reconstruction: a review.

作者信息

Choi Kihwan

机构信息

Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811 Republic of Korea.

出版信息

Biomed Eng Lett. 2024 Sep 12;14(6):1207-1220. doi: 10.1007/s13534-024-00424-w. eCollection 2024 Nov.

Abstract

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

摘要

本文综述了用于CT图像去噪和重建的自监督学习方法。目前,深度学习已成为医学成像以及计算机视觉中的主导工具。特别是,自监督学习方法作为一种无需干净/有噪声参考即可学习CT图像的技术,已引起了广泛关注。在简要回顾CT图像去噪和重建的基本原理之后,我们研究了深度学习在CT图像去噪和重建方面的进展。最后,我们重点关注用于图像去噪和重建的自监督学习的理论和方法演变。

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Self-supervised learning for CT image denoising and reconstruction: a review.用于CT图像去噪和重建的自监督学习:综述
Biomed Eng Lett. 2024 Sep 12;14(6):1207-1220. doi: 10.1007/s13534-024-00424-w. eCollection 2024 Nov.

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IEEE Trans Med Imaging. 2023 Apr;42(4):910-921. doi: 10.1109/TMI.2022.3219856. Epub 2023 Apr 3.
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Transformer With Double Enhancement for Low-Dose CT Denoising.用于低剂量CT去噪的双增强Transformer
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