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关于医学图像分析深度学习研究的全面综述,重点关注迁移学习。

A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning.

作者信息

Atasever Sema, Azginoglu Nuh, Terzi Duygu Sinanc, Terzi Ramazan

机构信息

Computer Engineering Department, Nevsehir Hacı Bektas Veli University, Nevsehir, Turkey.

Computer Engineering Department, Kayseri University, Kayseri, Turkey.

出版信息

Clin Imaging. 2023 Feb;94:18-41. doi: 10.1016/j.clinimag.2022.11.003. Epub 2022 Nov 12.


DOI:10.1016/j.clinimag.2022.11.003
PMID:36462229
Abstract

This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.

摘要

本次调查旨在识别该领域常用的方法、数据集、未来趋势、知识差距、限制因素和局限性,以便在迁移学习(TL)快速发展的同时,概述医学图像分析中当前使用的解决方案。与以往研究不同,本次调查根据不同解剖区域对2017年1月至2021年2月期间最近五年的当前研究进行了分组,并详细介绍了医学成像中使用的模态、医学任务、TL方法、源数据、目标数据以及公共或私有数据集。此外,它还为读者提供了有关技术挑战、机遇和未来研究趋势的详细信息。通过这种方式,提供了近期发展的概述,以帮助研究人员选择最有效和高效的方法,并获取广泛使用的公开可用医学数据集、研究差距以及现有文献的局限性。

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