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Advances in deep learning have led to remarkable success in augmented microscopy, enabling us to obtain high-quality microscope images without using expensive microscopy hardware and sample preparation techniques.
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Ronneberger, O., Fischer, P. & Brox, T · 2015
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Deep learning achieves super-resolution in fluorescence microscopy
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3d u-net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O · 2016
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Assessing phototoxicity in live fluorescence imaging
Laissue, P. P., Alghamdi, R. A., Tomancak, P., Reynaud, E. G. & Shroff, H · 2017
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Phototoxicity in live fluorescence microscopy, and how to avoid it
Icha, J., Weber, M., Waters, J. C. & Norden, C · 2017
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Building a 3D integrated cell
Johnson, G. R., Donovan-Maiye, R. M. & Maleckar, M. M · 2017
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Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction
Ounkomol, C. et al · 2017
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GANs for biological image synthesis
Osokin, A., Chessel, A., Carazo Salas, R. E. & Vaggi, F · 2017
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Non-local neural networks
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Range scaling global u-net for perceptual image enhancement on mobile devices
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Attention u-net: Learning where to look for the pancreas
Oktay, O. et al · 2018
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Unet++: A nested u-net architecture for medical image segmentation
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An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
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Deep learning for cellular image analysis
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Yuan, H. et al · 2019
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Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
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Deep learning enables cross-modality super-resolution in fluorescence microscopy
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U-net: deep learning for cell counting, detection, and morphometry
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Ce-net: context encoder network for 2d medical image segmentation
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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
Rivenson, Y. et al · 2019
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