Research progress on medical image dataset expansion methods / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 185-192, 2023.
Article
em Zh
| WPRIM
| ID: wpr-970690
Biblioteca responsável:
WPRO
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.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Diagnóstico por Imagem
/
Diagnóstico por Computador
/
Conjuntos de Dados como Assunto
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2023
Tipo de documento:
Article