[1] Marcu, L. Fluorescence lifetime techniques in medical applications. Ann. Biomed. Eng. 40, 304-331 (2012). doi: 10.1007/s10439-011-0495-y
[2] Hanley, Q. S., Arndt-Jovin, D. J. & Jovin, T. M. Spectrally resolved fluorescence lifetime imaging microscopy. Appl. Spectrosc. 56, 155-166 (2002). doi: 10.1366/0003702021954610
[3] Pian, Q. et al. Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging. Nat. Photon. 11, 411-414 (2017). doi: 10.1038/nphoton.2017.82
[4] Donoho, D. L. Compressed sensing. IEEE Trans. Inf. Theory 52, 1289-1306 (2006). doi: 10.1109/TIT.2006.871582
[5] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015). doi: 10.1038/nature14539
[6] Yang, Q. S. et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37, 1348-1357 (2018). doi: 10.1109/TMI.2018.2827462
[7] Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems 1097-1105 (Curran Associates Inc., Lake Tahoe, 2012).
[8] Dong, C. et al. Learning a deep convolutional network for image super-resolution. In Proceedings of the 13th European Conference on Computer Vision 184-199 (Springer, Zurich, 2014).
[9] Kulkarni, K. et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 449-458 (IEEE, Las Vegas, 2016).
[10] Mousavi, A. & Baraniuk, R. G. Learning to invert: Signal recovery via deep convolutional networks. In Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing 2272-2276 (IEEE, New Orleans, 2017).
[11] Yao, H. T. et al. DR2-net: deep residual reconstruction network for image compressive sensing. arXiv Prepr. arXiv 1702, 05743 (2017).
[12] Greenspan, H., van Ginneken, B. & Summers, R. M. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35, 1153-1159 (2016). doi: 10.1109/TMI.2016.2553401
[13] Chen, C. L. et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471 (2016). doi: 10.1038/srep21471
[14] Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437-1443 (2017). doi: 10.1364/OPTICA.4.001437
[15] Li, C. B. Compressive sensing for 3D data processing tasks: applications, models and algorithms. PhD thesis, Rice University Houston, TX, USA (2011).
[16] He, K. M. et al. Deep residual learning for image recognition. In Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition 770-778 (IEEE, Las Vegas, 2016).
[17] Cohen, G. et al. EMNIST: an extension of MNIST to handwritten letters. arXiv Prepr. arXiv 1702, 05373 (2017).
[18] Chollet, F. "Keras" (2015). (This reference cannot be found online. Please check)
[19] Abadi, M. et al. Tensorflow: a system for large-scale machine learning. In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation 265-283 (USENIX Association, Savannah, 2016).
[20] Ruder, S. An overview of gradient descent optimization algorithms. arXiv Prepr. arXiv 1609, 04747 (2016).
[21] van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605 (2008).
[22] Ochoa, M. et al. Assessing patterns for compressive fluorescence lifetime imaging. Opt. Lett. 43, 4370-4373 (2018). doi: 10.1364/OL.43.004370