Small training dataset convolutional neural networks for application-specific super-resolution microscopy

Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to trai...

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Veröffentlicht in:	Journal of biomedical optics Jg. 28; H. 3; S. 036501
Hauptverfasser:	Mannam, Varun, Howard, Scott
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Society of Photo-Optical Instrumentation Engineers 01.03.2023 SPIE S P I E - International Society for
Schlagworte:	Animals Artificial neural networks Cattle Coders Computer architecture Data interpretation Datasets Encoders-Decoders Endothelial Cells Fibroblasts Fluorescence Fluorescence microscopy Humans Image processing Image Processing, Computer-Assisted - methods Image resolution Information retrieval Localization Machine Learning Magnetic Resonance Imaging - methods Microscopy Microscopy - methods Neural networks Neural Networks, Computer Pulmonary arteries Pulmonary artery Research methodology Signal to noise ratio System effectiveness Training generative adversarial networks fully convolutional networks small datasets super-resolution dense encoder-decoder biomedical imaging diffraction-limited convolutional neural networks machine learning fluorescence microscopy dense layer
ISSN:	1083-3668, 1560-2281, 1560-2281
Online-Zugang:	Volltext
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