Application of DDPM in artificial intelligence image data augmentation of medical device
10.3969/j.issn.1672-8270.2024.03.031
- VernacularTitle:去噪扩散概率模型在人工智能医疗器械影像数据增广中的应用
- Author:
Pengfei HAO
1
;
Qingyu LI
;
Rui CHAI
;
Xi CHEN
;
Qinghua SONG
;
Naishui HAN
;
Ke ZHANG
Author Information
1. 山东省医疗器械和药品包装检验研究院医用电器质量评价中心 济南 250101
- Keywords:
Denoising diffusion probabilistic model(DDPM);
Medical device;
Artificial intelligence,Data augmentation
- From:
China Medical Equipment
2024;21(3):154-158
- CountryChina
- Language:Chinese
-
Abstract:
Medical device imaging data augmentation is a method of expanding existing datasets by generating new data samples,which is of great significance for improving the performance of artificial intelligence(AI)medical device-related models and clinical application effects.However,traditional data augmentation methods are usually limited by the quality,realism,and diversity of generated samples.Denoising diffusion probabilistic model(DDPM)is a generative model based on the noise diffusion process,and its main idea is to generate samples with high quality by modelling the sampling process of the target distribution as a process of progressive denoising from the noise distribution.The basic principles and working mechanisms of DDPM were reviewed,the application scenarios of this method in AI medical device data augmentation were analyzed,and its advantages,challenges,and future development directions were explored to provide a reference for the field of AI medical device data augmentation.
