Continual medical image denoising based on triplet neural networks collaboration

被引：0
|
作者：
Zeng, Xianhua [1 ]
Guo, Yongli [1 ]
Li, Laquan [2 ]
Liu, Yuhang [1 ]
机构：
[1] School of Computer Science and Technology/School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing,400065, China
[2] School of Science, Chongqing University of Posts and Telecommunications, Chongqing,400065, China
来源：
Computers in Biology and Medicine | 2024年 / 179卷
关键词：
Background: When multiple tasks are learned consecutively; the old model parameters may be overwritten by the new data; resulting in the phenomenon that the new task is learned and the old task is forgotten; which leads to catastrophic forgetting. Moreover; continual learning has no mature solution for image denoising tasks. Methods: Therefore; in order to solve the problem of catastrophic forgetting caused by learning multiple denoising tasks; we propose a Triplet Neural-networks Collaboration-continuity DeNosing (TNCDN) model. Use triplet neural networks to update each other cooperatively. The knowledge from two denoising networks that maintain continual learning capability is transferred to the main-denoising network. The main-denoising network has new knowledge and can consolidate old knowledge. A co-training mechanism is designed. The main-denoising network updates the other two denoising networks with different thresholds to maintain memory reinforcement capability and knowledge extension capability. Results: The experimental results show that our method effectively alleviates catastrophic forgetting. In GS; CT and ADNI datasets; compared with ANCL; the TNCDN(PromptIR) method reduced the average degree of forgetting on the evaluation index PSNR by 2.38 (39%) and RMSE by 1.63 (55%). Conclusion: This study aims to solve the problem of catastrophic forgetting caused by learning multiple denoising tasks. Although the experimental results are promising; extending the basic denoising model to more data sets and tasks will enhance its application. Nevertheless; this study is a starting point; which can provide reference and support for the further development of continuous learning image denoising task. © 2024 Elsevier Ltd;
D O I：
10.1016/j.compbiomed.2024.108914
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