Survey
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1291-1300

Survey of medical image analysis domain adaptation based on deep learning

Li Jiaxi
Liu Hongying
Wan Liang
Academy of Medical Engineering & Translational Medicine, Tianjin University, Tianjin 300072, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0379

Abstract

The wide application of deep learning techniques has strongly promoted the development of the medical image analysis field. Most deep learning methods usually assume that the training and test sets are independent and identically distributed. However, the assumption is problematic to guarantee when the model. Resulting in the dilemma of model performance degradation and poor scene generalization ability. Deep learning-based domain adaptation techniques are the mainstream methods for improving model migration ability, which aims to enable the model trained on one dataset to obtain better results on another dataset with no or only a small amount of labels. Due to the difficulties in sample acquisition and labelling, unique image properties and modal differences in medical images, it brings many practical challenges to domain adaptive technology. This paper firstly introduced the definition and primary challenges of the domain adaptation and then classified and summarized related algorithms in recent years from a technical point of view, compared and analyzed their advantages and disadvantages, and then introduced the medical image datasets commonly used in domain adaptation and related algorithm results in detail. Finally, this paper prospected the future research direction of domain adaptation for medical image analysis regarding development bottlenecks, technical means, and cross-cutting areas.

Foundation Support

天津市自然科学基金资助项目(21JCYBJC00510)
天津市研究生科研创新项目(2022SKY081)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0379
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Survey
Pages: 1291-1300
Serial Number: 1001-3695(2024)05-002-1291-10

Publish History

[2023-11-01] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

李佳燨, 刘红英, 万亮. 基于深度学习的医学图像分析域自适应研究 [J]. 计算机应用研究, 2024, 41 (5): 1291-1300. (Li Jiaxi, Liu Hongying, Wan Liang. Survey of medical image analysis domain adaptation based on deep learning [J]. Application Research of Computers, 2024, 41 (5): 1291-1300. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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