[1] Chantziantoniou N, Donnelly A D, Mukherjee M, et al. Inception and development of the papanicolaou stain method [J]. Acta Cytologica, 2017, 61(4-5): 266-280. doi:  10.1159/000457827
[2] Fischer A H, Jacobson K A, Rose J, et al. Hematoxylin and eosin staining of tissue and cell sections [J]. Cold Spring Harbor Protocols, 2008, 2008(5): pdb.prot4986. doi:  10.1101/pdb.prot4986
[3] Beutner E H. Immunofluorescent staining: The fluorescent antibody method [J]. Bacteriological Reviews, 1961, 25(1): 49-76. doi:  10.1128/br.25.1.49-76.1961
[4] Irshad H, Veillard A, Roux L, et al. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review—current status and future potential [J]. IEEE Reviews in Biomedical Engineering, 2013, 7: 97-114.
[5] Chari S T, Echelmeyer S. Can histopathology be the “Gold Standard” for diagnosing autoimmune pancreatitis? [J]. Gastro-enterology, 2005, 129(6): 2118-2120. doi:  10.1053/j.gastro.2005.10.034
[6] Onder D, Zengin S, Sarioglu S. A review on color normalization and color deconvolution methods in histopathology [J]. Applied Immunohistochemistry & Molecular Morphology, 2014, 22(10): 713-719.
[7] De Matos J, Britto Jr A S, Oliveira L E S, et al. Histopathologic image processing: A review [J]. arXiv preprint, 2019: 1904.07900.
[8] Ursache R, Andersen T G, Marhavý P, et al. A protocol for combining fluorescent proteins with histological stains for diverse cell wall components [J]. The Plant Journal, 2018, 93(2): 399-412.
[9] Taqi S A, Sami S A, Sami L B, et al. A review of artifacts in histopathology [J]. Journal of Oral and Maxillofacial Pathology: JOMFP, 2018, 22(2): 279.
[10] Celis R, Romero E. Unsupervised color normalisation for H and E stained histopathology image analysis[C]//11th International Symposium on Medical Information Processing and Analysis. International Society for Optics and Photonics, 2015, 9681: 968104.
[11] Abraham T, Shaw A, O'Connor D, et al. Slide-free MUSE microscopy to H&E histology modality conversion via unpaired image-to-image translation GAN models [J]. arXiv preprint, 2020: 2008.08579.
[12] de Haan K, Rivenson Y, Wu Y, et al. Deep-learning-based image reconstruction and enhancement in optical microscopy [J]. Proceedings of the IEEE, 2019, 108(1): 30-50.
[13] Liang H, Plataniotis K N, Li X. Stain style transfer of histopathology images via structure-preserved generative learning[C]//International Workshop on Machine Learning for Medical Image Reconstruction. Cham: Springer, 2020: 153-162.
[14] Mahapatra D, Bozorgtabar B, Thiran J P, et al. Structure preserving stain normalization of histopathology images using self supervised semantic guidance[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2020: 309-319.
[15] Shi M, McMillan K L, Wu J, et al. Cisplatin nephrotoxicity as a model of chronic kidney disease [J]. Laboratory Investigation, 2018, 98(8): 1105-1121.
[16] Rivenson Y, Göröcs Z, Günaydin H, et al. Deep learning microscopy [J]. Optica, 2017, 4(11): 1437-1443. doi:  10.1364/OPTICA.4.001437
[17] Bilgin C C, Rittscher J, Filkins R, et al. Digitally adjusting chromogenic dye proportions in brightfield microscopy images [J]. Journal of Microscopy, 2012, 245(3): 319-330. doi:  10.1111/j.1365-2818.2011.03579.x
[18] Veta M, Pluim J P W, Van Diest P J, et al. Breast cancer histopathology image analysis: A review [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(5): 1400-1411. doi:  10.1109/TBME.2014.2303852
[19] Hooja S, Pal N, Malhotra B, et al. Comparison of Ziehl Neelsen & Auramine O staining methods on direct and concentrated smears in clinical specimens [J]. The Indian Journal of Tuberculosis, 2011, 58(2): 72-76.
[20] Mak K K, Pichika M R. Artificial intelligence in drug development: present status and future prospects [J]. Drug Discovery Today, 2019, 24(3): 773-780. doi:  10.1016/j.drudis.2018.11.014
[21] Gunčar G, Kukar M, Notar M, et al. An application of machine learning to haematological diagnosis [J]. Scientific Reports, 2018, 8(1): 1-12.
[22] Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery [J]. Drug Discovery Today, 2018, 23(6): 1241-1250. doi:  10.1016/j.drudis.2018.01.039
[23] Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians [J]. European Journal of Internal Medicine, 2018, 48: e13-e14. doi:  10.1016/j.ejim.2017.06.017
[24] Grys B T, Lo D S, Sahin N, et al. Machine learning and computer vision approaches for phenotypic profiling [J]. Journal of Cell Biology, 2017, 216(1): 65-71. doi:  10.1083/jcb.201610026
[25] Rivenson Y, Liu T, Wei Z, et al. PhaseStain: The digital staining of label-free quantitative phase microscopy images using deep learning [J]. Light: Science & Applications, 2019, 8(1): 1-11. doi:  10.1038/s41377-019-0129-y
[26] Shaban M T, Baur C, Navab N, et al. Staingan: Stain style transfer for digital histological images[C]//2019 IEEE 16 th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 953-956.
[27] Lizzi F L, Astor M, Liu T, et al. Ultrasonic spectrum analysis for tissue assays and therapy evaluation [J]. International Journal of Imaging Systems and Technology, 1997, 8(1): 3-10. doi:  10.1002/(SICI)1098-1098(1997)8:1<3::AID-IMA2>3.0.CO;2-E
[28] Linzer M, Norton S J. Ultrasonic tissue characterization [J]. Annual Review of Biophysics and Bioengineering, 1982, 11(1): 303-329. doi:  10.1146/annurev.bb.11.060182.001511
[29] Xu M, Wang L V. Photoacoustic imaging in biomedicine [J]. Review of Scientific Instruments, 2006, 77(4): 041101. doi:  10.1063/1.2195024
[30] Diem M, Chiriboga L, Yee H. Infrared spectroscopy of human cells and tissue. VIII. Strategies for analysis of infrared tissue mapping data and applications to liver tissue [J]. Biopolymers: Original Research on Biomolecules, 2000, 57(5): 282-290.
[31] Xiao X, Ma L. Color transfer in correlated color space[C]//Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and Its Applications, 2006: 305-309.
[32] Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images [J]. IEEE Computer Graphics and Applications, 2001, 21(5): 34-41.
[33] Welsh T, Ashikhmin M, Mueller K. Transferring color to greyscale images[C]//Proceedings of the 29 th Annual Conference on Computer Graphics and Interactive Techniques, 2002: 277-280.
[34] Byra M, Galperin M, Ojeda-Fournier H, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion [J]. Medical Physics, 2019, 46(2): 746-755. doi:  10.1002/mp.13361
[35] Hwang Y, Lee J Y, So Kweon I, et al. Color transfer using probabilistic moving least squares[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 3342-3349.
[36] Jing Y, Yang Y, Feng Z, et al. Neural style transfer: A review [J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 26(11): 3365-3385.
[37] Wan S, Xia Y, Qi L, et al. Automated colorization of a grayscale image with seed points propagation [J]. IEEE Transactions on Multimedia, 2020, 22(7): 1756-1768. doi:  10.1109/TMM.2020.2976573
[38] Gatys L A, Ecker A S, Bethge M. A neural algorithm of artistic style [J]. arXiv preprint, 2015: 1508.06576.
[39] Albawi S, Mohammed T A, Al-Zawi S. Understanding of a convolutional neural network[C]//2017 International Conference on Engineering and Technology (ICET). IEEE, 2017: 1-6.
[40] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
[41] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
[42] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
[43] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [J]. arXiv preprint, 2014: 1409.1556.
[44] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [J]. Advances in Neural Information Processing Systems, 2012, 25: 1097-1105.
[45] Li Y, Hao Z B, Lei H. Survey of convolutional neural network [J]. Journal of Computer Applications, 2016, 36(9): 2508-2515.
[46] Pineda F J. Generalization of back-propagation to recurrent neural networks [J]. Physical Review Letters, 1987, 59(19): 2229. doi:  10.1103/PhysRevLett.59.2229
[47] Johnson J, Alahi A, Li Feifei. Perceptual losses for real-time style transfer and super-resolution[C]//European Conference on Computer Vision. Cham: Springer, 2016: 694-711.
[48] Liu X C, Cheng M M, Lai Y K, et al. Depth-aware neural style transfer[C]//Proceedings of the Symposium on Non-Photorealistic Animation and Rendering, 2017: 1-10.
[49] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems, 2014 : 2672-2680.
[50] Liu H, Fu Z, Han J, et al. Single satellite imagery simultaneous super-resolution and colorization using multi-task deep neural networks [J]. Journal of Visual Communication and Image Representation, 2018, 53: 20-30. doi:  10.1016/j.jvcir.2018.02.016
[51] Izadyyazdanabadi M, Belykh E, Zhao X, et al. Fluorescence image histology pattern transformation using image style transfer [J]. Frontiers in Oncology, 2019, 9: 519. doi:  10.3389/fonc.2019.00519
[52] Rivenson Y, Wang H, Wei Z, et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning [J]. Nature Biomedical Engineering, 2019, 3(6): 466-477. doi:  10.1038/s41551-019-0362-y
[53] Rivenson Y, de Haan K, Wallace W D, et al. Emerging advances to transform histopathology using virtual staining [J]. BME Frontiers, 2020, 2020: 9647163. doi:  10.34133/2020/9647163
[54] Nishar H, Chavanke N, Singhal N. Histopathological stain transfer using style transfer network with adversarial loss[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2020: 330-340.
[55] Touvron H, Douze M, Cord M, et al. Powers of layers for image-to-image translation [J]. arXiv preprint, 2020: 2008.05763.
[56] Zuo Z, Xu Q, Zhang H, et al. Multimodal image-to-image translation via mutual information estimation and maximization [J]. arXiv preprint, 2020: 2008.03529.
[57] Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1125-1134.
[58] Dai B, Fidler S, Urtasun R, et al. Towards diverse and natural image descriptions via a conditional gan[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2970-2979.
[59] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2223-2232.
[60] Almahairi A, Rajeshwar S, Sordoni A, et al. Augmented cyclegan: Learning many-to-many mappings from unpaired data[C]//International Conference on Machine Learning. PMLR, 2018: 195-204.
[61] Tschuchnig M E, Oostingh G J, Gadermayr M. Generative adversarial networks in digital pathology: A survey on trends and future potential [J]. Patterns, 2020, 1(6): 100089. doi:  10.1016/j.patter.2020.100089
[62] Petriceks A H, Olivas J C, Srivastava S. Trends in geriatrics graduate medical education programs and positions, 2001 to 2018 [J]. Gerontology and Geriatric Medicine, 2018, 4: 2333721418777659. doi:  10.1177/2333721418777659
[63] Shen D, Wu G, Suk H I. Deep learning in medical image analysis [J]. Annual Review of Biomedical Engineering, 2017, 19: 221-248. doi:  10.1146/annurev-bioeng-071516-044442
[64] Barcia J J. The Giemsa stain: Its history and applications [J]. International Journal of Surgical Pathology, 2007, 15(3): 292-296. doi:  10.1177/1066896907302239
[65] Daykin M E, Hussey R S. Staining and histopathological techniques [J]. An Advanced Treatise on Meloidogyne, 1985, 2: 39-48.
[66] Pradhan P, Meyer T, Vieth M, et al. Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning [J]. Biomedical Optics Express, 2021, 12(4): 2280-2298. doi:  10.1364/BOE.415962
[67] Barlow H B. Unsupervised learning [J]. Neural Computation, 1989, 1(3): 295-311. doi:  10.1162/neco.1989.1.3.295
[68] Teramoto A, Yamada A, Tsukamoto T, et al. Mutual stain conversion between Giemsa and Papanicolaou in cytological images using cycle generative adversarial network [J]. Heliyon, 2021, 7(2): e06331. doi:  10.1016/j.heliyon.2021.e06331
[69] Padma S, PV R, Kante R, et al. A comparative study of Staining characteristics of Leishman-Geimsa cocktail and Papanicolaou stain in Cervical Cytology [J]. Asian Pacific Journal of Health Sciences, 2018, 5: 233-236. doi:  10.21276/apjhs.2018.5.3.32
[70] Gollapudi B, Kamra O P. Applications of a simple Giemsa-staining method in the micronucleus test [J]. Mutat Res, 1979, 64(1): 45-46. doi:  10.1016/0165-1161(79)90135-3
[71] Woolman M, Tata A, Bluemke E, et al. An assessment of the utility of tissue smears in rapid cancer profiling with desorption electrospray ionization mass spectrometry (DESI-MS) [J]. Journal of The American Society for Mass Spectrometry, 2016, 28(1): 145-153.
[72] Niazi M K K, Parwani A V, Gurcan M N. Digital pathology and artificial intelligence [J]. The Lancet Oncology, 2019, 20(5): e253-e261. doi:  10.1016/S1470-2045(19)30154-8
[73] Lo Y C, Chung I F, Guo S N, et al. Cycle-consistent GAN-based stain translation of renal pathology images with glomerulus detection application [J]. Applied Soft Computing, 2021, 98: 106822. doi:  10.1016/j.asoc.2020.106822
[74] Khojasteh M, Ward R, MacAulay C. Quantification of membrane IHC stains through multi-spectral imaging[C]//2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2012: 752-755.
[75] Xu Z, Moro C F, Bozóky B, et al. GAN-based virtual re-staining: A promising solution for whole slide image analysis [J]. arXiv preprint, 2019: 1901.04059.
[76] de Bel T, Bokhorst J M, van der Laak J, et al. Residual cyclegan for robust domain transformation of histopathological tissue slides [J]. Medical Image Analysis, 2021, 70: 102004. doi:  10.1016/j.media.2021.102004
[77] Bornstein M B. Reconstituted rat-tail collagen used as substrate for tissue cultures on coverslips in Maximow slides and roller tubes [J]. Laboratory Investigation, 1958, 7(2): 134-137.
[78] Boonstra H, Oosterhuis J W, Oosterhuis A M, et al. Cervical tissue shrinkage by formaldehyde fixation, paraffin wax embedding, section cutting and mounting [J]. Virchows Archiv A, 1983, 402(2): 195-201. doi:  10.1007/BF00695061
[79] Jensen E C. Quantitative analysis of histological staining and fluorescence using ImageJ [J]. The Anatomical Record, 2013, 296(3): 378-381. doi:  10.1002/ar.22641
[80] Kang L, Li X, Zhang Y, et al. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining [J]. Photoacoustics, 2021, 25: 100308. doi:  10.1016/j.pacs.2021.100308
[81] Baik J W, Kim H, Son M, et al. Intraoperative label-free photoacoustic histopathology of clinical specimens [J]. Laser & Photonics Reviews, 2021, 15(10): 2100124.
[82] Morrison D, Harris-Birtill D, Caie P D. Generative deep learning in digital pathology workflows [J]. The American Journal of Pathology, 2021, 191(10): 1717-1723. doi:  10.1016/j.ajpath.2021.02.024
[83] Zhou N, Cai D, Han X, et al. Enhanced cycle-consistent generative adversarial network for color normalization of H&E stained images[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2019: 694-702.
[84] Cho H, Lim S, Choi G, et al. Neural stain-style transfer learning using gan for histopathological images [J]. arXiv preprint, 2017: 1710.08543.
[85] Li B, Keikhosravi A, Loeffler A G, et al. Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization [J]. Medical Image Analysis, 2021, 68: 101938. doi:  10.1016/j.media.2020.101938
[86] Chen X, Yu J, Cheng S, et al. An unsupervised style normalization method for cytopathology images [J]. Computational and Structural Biotechnology Journal, 2021, 19: 3852-3863. doi:  10.1016/j.csbj.2021.06.025
[87] Vahadane A, Peng T, Sethi A, et al. Structure-preserving color normalization and sparse stain separation for histological images [J]. IEEE Transactions on Medical Imaging, 2016, 35(8): 1962-1971. doi:  10.1109/TMI.2016.2529665
[88] Macenko M, Niethammer M, Marron J S, et al. A method for normalizing histology slides for quantitative analysis[C]//2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009: 1107-1110.
[89] Khan A M, Rajpoot N, Treanor D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution [J]. IEEE Transactions on Biomedical Engineering, 2014, 61(6): 1729-1738. doi:  10.1109/TBME.2014.2303294
[90] Gupta A, Duggal R, Gehlot S, et al. GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images [J]. Medical Image Analysis, 2020, 65: 101788. doi:  10.1016/j.media.2020.101788
[91] Zheng Y, Jiang Z, Zhang H, et al. Adaptive color deconvolution for histological WSI normalization [J]. Computer Methods and Programs in Biomedicine, 2019, 170: 107-120. doi:  10.1016/j.cmpb.2019.01.008
[92] Shaban M T, Baur C, Navab N, et al. Staingan: Stain style transfer for digital histological images[C]//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 953-956.
[93] Tellez D, Litjens G, Bándi P, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology [J]. Medical Image Analysis, 2019, 58: 101544. doi:  10.1016/j.media.2019.101544
[94] Bian Y, Jiang Y, Wang J, et al. Deep learning colorful ptychographic iterative engine lens-less diffraction microscopy [J]. Optics and Lasers in Engineering, 2022, 150: 106843. doi:  10.1016/j.optlaseng.2021.106843
[95] Shen H, Gao J. Deep learning virtual colorful lens-free on-chip microscopy [J]. Chinese Optics Letters, 2020, 18(12): 121705. doi:  10.3788/COL202018.121705
[96] Bian Y, Jiang Y, Huang Y, et al. Deep learning virtual colorization overcoming chromatic aberrations in singlet lens microscopy [J]. APL Photonics, 2021, 6(3): 031301. doi:  10.1063/5.0039206