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

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.