@article {69558,
	title = {Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.},
	journal = {Sci Rep},
	volume = {14},
	year = {2024},
	month = {2024 Aug 06},
	pages = {18184},
	abstract = {<p>Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.</p>
},
	issn = {2045-2322},
	doi = {10.1038/s41598-024-68918-2},
	url = {https://doi.org/10.1038/s41598-024-68918-2},
	author = {Rehman, Azaan and Zhovmer, Alexander and Sato, Ryo and Mukouyama, Yoh-Suke and Chen, Jiji and Rissone, Alberto and Puertollano, Rosa and Liu, Jiamin and Vishwasrao, Harshad D and Shroff, Hari and Combs, Christian A and Xue, Hui}
}
