[1] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015). doi: 10.1038/nature14539
[2] Barbastathis, G., Ozcan, A. & Situ, G. On the use of deep learning for computational imaging. Optica 6, 921-943 (2019). doi: 10.1364/OPTICA.6.000921
[3] Moen, E. et al. Deep learning for cellular image analysis. Nat. Methods 16, 1233-1246 (2019). doi: 10.1038/s41592-019-0403-1
[4] Hornik, K., Stinchcombe, M. & White, H. Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359-366 (1989). doi: 10.1016/0893-6080(89)90020-8
[5] Belthangady, C. & Royer, L. A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16, 1215-1225 (2019). doi: 10.1038/s41592-019-0458-z
[6] Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015, 234-241.
[7] Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67-70 (2019). doi: 10.1038/s41592-018-0261-2
[8] Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090-1097 (2018). doi: 10.1038/s41592-018-0216-7
[9] Ounkomol, C. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917-920 (2018). doi: 10.1038/s41592-018-0111-2
[10] Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437-1443 (2017). doi: 10.1364/OPTICA.4.001437
[11] Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792-803. e19 (2018). doi: 10.1016/j.cell.2018.03.040
[12] Goodfellow, I. J. et al. Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems. Long Beach, USA: NIPS, 2014, 2672-2680.
[13] Isola, P. et al. Image-to-image translation with conditional adversarial networks. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017, 5967-5976.
[14] Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103-110 (2019). doi: 10.1038/s41592-018-0239-0
[15] Ouyang, W. et al. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460-468 (2018). doi: 10.1038/nbt.4106
[16] Wu, Y. C. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light. : Sci. Appl. 8, 25 (2019). doi: 10.1038/s41377-019-0139-9
[17] Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3, 466-477 (2019). doi: 10.1038/s41551-019-0362-y
[18] Zhu, J. Y. et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017, 2242-2251.
[19] Zhang, Y. B. et al. Multiple cycle-in-cycle generative adversarial networks for unsupervised image super-resolution. IEEE Trans. Image Process. 29, 1101-1112 (2019). http://ieeexplore.ieee.org/document/8825849
[20] Choi, Y. et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018, 8789-8797.
[21] Yi, Z. L. et al. DualGAN: unsupervised dual learning for image-to-image translation. Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017, 2868-2876.
[22] Kang, E. et al. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med. Phys. 46, 550-562 (2019). doi: 10.1002/mp.13284
[23] You, C. Y. et al. CT super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). IEEE Transactions on Medical. Imaging 39, 188-203 (2020). doi: 10.1109/TMI.2019.2922960
[24] Sim, B. et al. Optimal transport driven CycleGAN for unsupervised learning in inverse problems (2019). at https://arxiv.org/abs/1909.12116.
[25] Choi, G. et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt. Express 27, 4927-4943 (2019). doi: 10.1364/OE.27.004927
[26] Ihle, S. J. et al. Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networks. Nat. Mach. Intell. 1, 461-470 (2019). doi: 10.1038/s42256-019-0096-2
[27] Gharesifard, B. & Cortés, J. Distributed convergence to Nash equilibria in two-network zero-sum games. Automatica 49, 1683-1692 (2013). doi: 10.1016/j.automatica.2013.02.062
[28] Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559-1567 (2018). doi: 10.1038/s41591-018-0177-5
[29] Lu, F. K. et al. Label-free neurosurgical pathology with stimulated Raman imaging. Cancer Res. 76, 3451-3462 (2016). doi: 10.1158/0008-5472.CAN-16-0270
[30] Orringer, D. A. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. 1, 0027 (2017). doi: 10.1038/s41551-016-0027
[31] Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26, 52-58 (2020). doi: 10.1038/s41591-019-0715-9
[32] Zhang, Y. H. et al. PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets (2020). at https://arxiv.org/abs/2011.08660.
[33] Johnson, J., Alahi, A. & Li, F. F. Perceptual losses for real-time style transfer and super-resolution. Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016, 694-711.
[34] Li, C. & Wand, M. Precomputed real-time texture synthesis with markovian generative adversarial networks. Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016, 702-716.
[35] Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization (2014). at https://arxiv.org/abs/1412.6980.
[36] Sirinukunwattana, K. et al. Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489-502 (2017). doi: 10.1016/j.media.2016.08.008
[37] Sirinukunwattana, K., Snead, D. R. J. & Rajpoot, N. M. A stochastic polygons model for glandular structures in colon histology images. IEEE Transactions on Medical. Imaging 34, 2366-2378 (2015). doi: 10.1109/TMI.2015.2433900
[38] Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247-1253 (2019). doi: 10.1038/s41592-019-0612-7