Research progress on medical image dataset expansion methods.
10.7507/1001-5515.202206039
- Author:
Ying CHEN
1
;
Hongping LIN
1
;
Wei ZHANG
1
;
Longfeng FENG
1
;
Cheng ZHENG
1
;
Taohui ZHOU
1
;
Zhen YI
2
;
Lan LIU
2
Author Information
1. School of Software, Nanchang Hangkong University, Nanchang 330063, P. R. China.
2. Department of Medical Imaging, Jiangxi Cancer Hospital, Nanchang 330029, P. R. China.
- Publication Type:Journal Article
- Keywords:
Computer aided diagnosis system;
Generative adversarial network;
Geometric transformation;
Medical image expansion
- MeSH:
Humans;
Diagnosis, Computer-Assisted;
Diagnostic Imaging;
Datasets as Topic
- From:
Journal of Biomedical Engineering
2023;40(1):185-192
- CountryChina
- Language:Chinese
-
Abstract:
Computer-aided diagnosis (CAD) systems play a very important role in modern medical diagnosis and treatment systems, but their performance is limited by training samples. However, the training samples are affected by factors such as imaging cost, labeling cost and involving patient privacy, resulting in insufficient diversity of training images and difficulty in data obtaining. Therefore, how to efficiently and cost-effectively augment existing medical image datasets has become a research hotspot. In this paper, the research progress on medical image dataset expansion methods is reviewed based on relevant literatures at home and abroad. First, the expansion methods based on geometric transformation and generative adversarial networks are compared and analyzed, and then improvement of the augmentation methods based on generative adversarial networks are emphasized. Finally, some urgent problems in the field of medical image dataset expansion are discussed and the future development trend is prospected.
