The Innovation, 6 October, 2025, DOI：https://doi.org/10.1016/j.xinn.2025.101123

Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging

Tianjie Yang, Jia He, Xian’ao Zhao, Congmin Ren, Zhuoli Ding, Lu Wang, Hanqing Zhao, Ling Chu, Siyuan Luo, Chaojing Shi, Lusheng Gu, Tao Xu, Ge Yang, Wei Ji

Abstract

In deep learning super-resolution microscopy, concerns exist about the generation of artifacts, and methods for artifact suppression are lacking. We developed a self-adaptive fine-tuning method that dynamically adjusts the parameters of the models to minimize the loss function, which includes direct quantification of artifacts from live-cell imaging. Integrating self-adaptive fine-tuning with super-resolution models enables significant artifact reduction in the visualization of nanoscale organelle interactions at high spatial-temporal resolution.

Article link：https://www.sciencedirect.com/science/article/pii/S2666675825003261?via%3Dihub