[HTML][HTML] A bird's-eye view of deep learning in bioimage analysis
E Meijering - Computational and structural biotechnology journal, 2020 - Elsevier
Deep learning of artificial neural networks has become the de facto standard approach to
solving data analysis problems in virtually all fields of science and engineering. Also in …
solving data analysis problems in virtually all fields of science and engineering. Also in …
[HTML][HTML] Imaging in focus: an introduction to denoising bioimages in the era of deep learning
Fluorescence microscopy enables the direct observation of previously hidden dynamic
processes of life, allowing profound insights into mechanisms of health and disease …
processes of life, allowing profound insights into mechanisms of health and disease …
Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit
A fundamental challenge in fluorescence microscopy is the photon shot noise arising from
the inevitable stochasticity of photon detection. Noise increases measurement uncertainty …
the inevitable stochasticity of photon detection. Noise increases measurement uncertainty …
A poisson-gaussian denoising dataset with real fluorescence microscopy images
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to
its inherently weak signal, fluorescence microscopy is not only much noisier than …
its inherently weak signal, fluorescence microscopy is not only much noisier than …
Fast and accurate sCMOS noise correction for fluorescence microscopy
The rapid development of scientific CMOS (sCMOS) technology has greatly advanced
optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view …
optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view …
Opportunities and challenges for deep learning in cell dynamics research
B Chai, C Efstathiou, H Yue, VM Draviam - Trends in Cell Biology, 2024 - cell.com
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer
vision and deep learning (DL) techniques for the evaluation of microscopy images and …
vision and deep learning (DL) techniques for the evaluation of microscopy images and …
Image de-noising with machine learning: A review
Images are susceptible to various kinds of noises, which corrupt the pictorial information
stored in the images. Image de-noising has become an integral part of the image processing …
stored in the images. Image de-noising has become an integral part of the image processing …
Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration
Advances in two two-photon microscopy (2PM) have made three-dimensional (3D) neural
imaging of deep cortical regions possible. However, 2PM often suffers from poor image …
imaging of deep cortical regions possible. However, 2PM often suffers from poor image …
Computational methods for single-cell imaging and omics data integration
Integrating single cell omics and single cell imaging allows for a more effective
characterisation of the underlying mechanisms that drive a phenotype at the tissue level …
characterisation of the underlying mechanisms that drive a phenotype at the tissue level …
A low-cost automated digital microscopy platform for automatic identification of diatoms
Featured Application Development of a fully operative low-cost automated digital
microscope for the detection of diatoms by applying deep learning. Abstract Currently …
microscope for the detection of diatoms by applying deep learning. Abstract Currently …