Deep learning-enabled virtual histological staining of biological samples

Bijie Bai; Xilin Yang; Yuzhu Li; Yijie Zhang; Nir Pillar; Aydogan Ozcan

doi:10.1038/s41377-023-01104-7

Go to Nature Website

Your Location：

Home >

Browse articles >

Deep learning-enabled virtual histological staining of biological samples

Review

- Deep learning-enabled virtual histological staining of biological samples
- Light: Science & Applications Vol. 12, Issue 3, Pages: 335-354(2023)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-023-01104-7
  CLC：
- Published：31 March 2023，
  
  Published Online：3 March 2023，
  
  Received：13 November 2022，
  
  Revised：10 February 2023，
  
  Accepted：14 February 2023
Scan for full text
Bai, B.J. et al. Deep learning-enabled virtual histological staining of biological samples. Light: Science & Applications, 12, 335-354 (2023).
DOI：

Bai, B.J. et al. Deep learning-enabled virtual histological staining of biological samples. Light: Science & Applications, 12, 335-354 (2023). DOI： 10.1038/s41377-023-01104-7.

摘要

Abstract

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research

which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However

the current histological staining workflow requires tedious sample preparation steps

specialized laboratory infrastructure

and trained histotechnologists

making it expensive

time-consuming

and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks

providing rapid

cost-effective

and accurate alternatives to standard chemical staining methods. These techniques

broadly referred to as

virtual staining

were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain

performing virtual stain-to-stain transformations. In this Review

we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced

followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field

aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.

关键词

Keywords

references

Bancroft, J. D. & Gamble, M. Theory and Practice of Histological Techniques. 6th edn. (Churchill Livingstone, Edinburgh, 2008).

G. Musumeci. Past, present and future: overview on histology and histopathology. J. Histol. Histopathol., 2014. 15DOI:10.7243/2055-091X-1-5http://doi.org/10.7243/2055-091X-1-5.

M. Titford. Progress in the development of microscopical techniques for diagnostic pathology. J. Histotechnol., 2009. 329-19. DOI:10.1179/his.2009.32.1.9http://doi.org/10.1179/his.2009.32.1.9.

H. A. Alturkistani, F. M. Tashkandi, Z. M. Mohammedsaleh. Histological stains: a literature review and case study. Global Journal of Health. Science, 2016. 872-79. .

M. N. Gurcan, 等. Histopathological image analysis: a review. IEEE Rev. Biomed. Eng., 2009. 2147-171. DOI:10.1109/RBME.2009.2034865http://doi.org/10.1109/RBME.2009.2034865.

M. Titford. The long history of hematoxylin. Biotech. Histochem., 2005. 8073-78. DOI:10.1080/10520290500138372http://doi.org/10.1080/10520290500138372.

N. C. Foot. The Masson trichrome staining methods in routine laboratory use. Stain Technol., 1933. 8101-110. DOI:10.3109/10520293309116112http://doi.org/10.3109/10520293309116112.

C. C. Pollitt. Basement membrane pathology: a feature of acute equine laminitis. Equine Vet. J., 1996. 2838-46. DOI:10.1111/j.2042-3306.1996.tb01588.xhttp://doi.org/10.1111/j.2042-3306.1996.tb01588.x.

A. H. Coons, H. J. Creech, R. N. Jones. Immunological properties of an antibody containing a fluorescent group. Exp. Biol. Med., 1941. 47200-202. DOI:10.3181/00379727-47-13084Phttp://doi.org/10.3181/00379727-47-13084P.

J. A. Ramos-Vara, M. A. Miller. When tissue antigens and antibodies get along: revisiting the technical aspects of immunohistochemistry—the red, brown, and blue technique. Vet. Pathol., 2014. 5142-87. DOI:10.1177/0300985813505879http://doi.org/10.1177/0300985813505879.

S. Al-Janabi, A. Huisman, P. J. Van Diest. Digital pathology: current status and future perspectives. Histopathology, 2012. 611-9. DOI:10.1111/j.1365-2559.2011.03814.xhttp://doi.org/10.1111/j.1365-2559.2011.03814.x.

A. Madabhushi, G. Lee. Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal., 2016. 33170-175. DOI:10.1016/j.media.2016.06.037http://doi.org/10.1016/j.media.2016.06.037.

H. D. Wang, 等. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 2019. 16103-110. DOI:10.1038/s41592-018-0239-0http://doi.org/10.1038/s41592-018-0239-0.

Y. C. Wu, 等. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light Sci. Appl., 2019. 825DOI:10.1038/s41377-019-0139-9http://doi.org/10.1038/s41377-019-0139-9.

Y. Jo, 等. Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning. Nat. Cell Biol., 2021. 231329-1337. DOI:10.1038/s41556-021-00802-xhttp://doi.org/10.1038/s41556-021-00802-x.

S. Y. Cheng, 等. Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy. Sci. Adv., 2021. 7eabe0431DOI:10.1126/sciadv.abe0431http://doi.org/10.1126/sciadv.abe0431.

C. Ounkomol, 等. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods, 2018. 15917-920. DOI:10.1038/s41592-018-0111-2http://doi.org/10.1038/s41592-018-0111-2.

Y. Rivenson, 等. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng., 2019. 3466-477. DOI:10.1038/s41551-019-0362-yhttp://doi.org/10.1038/s41551-019-0362-y.

Y. Rivenson, 等. Emerging advances to transform histopathology using virtual staining. BME Front., 2020. 20209647163DOI:10.34133/2020/9647163http://doi.org/10.34133/2020/9647163.

Y. Rivenson, 等. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl., 2019. 823DOI:10.1038/s41377-019-0129-yhttp://doi.org/10.1038/s41377-019-0129-y.

K. de Haan, 等. Deep learning-based transformation of H & E stained tissues into special stains. Nat. Commun., 2021. 124884DOI:10.1038/s41467-021-25221-2http://doi.org/10.1038/s41467-021-25221-2.

Y. J. Zhang, 等. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light Sci. Appl., 2020. 978DOI:10.1038/s41377-020-0315-yhttp://doi.org/10.1038/s41377-020-0315-y.

J. X. Li, 等. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci. Appl., 2021. 10233DOI:10.1038/s41377-021-00674-8http://doi.org/10.1038/s41377-021-00674-8.

Zhu, J. Y. et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). 2242–2251, https://doi.org/10.1109/ICCV.2017.244https://doi.org/10.1109/ICCV.2017.244 (IEEE, Venice, Italy, 2017).

Rivenson, Y. et al. Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue. Print at https://arxiv.org/abs/1803.11293https://arxiv.org/abs/1803.11293 (2018).

X. Y. Li, 等. Unsupervised content-preserving transformation for optical microscopy. Light Sci. Appl., 2021. 1044DOI:10.1038/s41377-021-00484-yhttp://doi.org/10.1038/s41377-021-00484-y.

A. Picon, 等. Autofluorescence image reconstruction and virtual staining for in-vivo optical biopsying. IEEE Access, 2021. 932081-32093. DOI:10.1109/ACCESS.2021.3060926http://doi.org/10.1109/ACCESS.2021.3060926.

X. Y. Meng, X. Li, X. Wang. A computationally virtual histological staining method to ovarian cancer tissue by deep generative adversarial networks. Comput. Math. Methods Med., 2021. 20214244157DOI:10.1155/2021/4244157http://doi.org/10.1155/2021/4244157.

M. Monici. Cell and tissue autofluorescence research and diagnostic applications. Biotechnol. Annu. Rev., 2005. 11227-256. DOI:10.1016/S1387-2656(05)11007-2http://doi.org/10.1016/S1387-2656(05)11007-2.

A. C. Croce, G. Bottiroli. Autofluorescence spectroscopy and imaging: a tool for biomedical research and diagnosis. Eur. J. Histochem., 2014. 582461.

B. J. Bai, 等. Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning. BME Front., 2022. 20229786242DOI:10.34133/2022/9786242http://doi.org/10.34133/2022/9786242.

Y. N. Nygate, 等. Holographic virtual staining of individual biological cells. Proc. Natl Acad. Sci. USA, 2020. 1179223-9231. DOI:10.1073/pnas.1919569117http://doi.org/10.1073/pnas.1919569117.

Abraham, T. M. et al. Mode-mapping qOBM microscopy to virtual hematoxylin and eosin (H & E) histology via deep learning. Proceedings of SPIE 12136, Unconventional Optical Imaging III. 121360Q (SPIE, Strasbourg, France, 2022).

N. Borhani, 等. Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy. Biomed. Opt. Express, 2019. 101339-1350. DOI:10.1364/BOE.10.001339http://doi.org/10.1364/BOE.10.001339.

P. Pradhan, 等. Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning. Biomed. Opt. Express, 2021. 122280-2298. DOI:10.1364/BOE.415962http://doi.org/10.1364/BOE.415962.

D. Li, 等. Deep Learning for virtual histological staining of bright-field microscopic images of unlabeled carotid artery tissue. Mol. Imaging Biol., 2020. 221301-1309. DOI:10.1007/s11307-020-01508-6http://doi.org/10.1007/s11307-020-01508-6.

G. H. Zhang, 等. Image-to-images translation for multiple virtual histological staining of unlabeled human carotid atherosclerotic tissue. Mol. Imaging Biol., 2022. 2431-41. DOI:10.1007/s11307-021-01641-whttp://doi.org/10.1007/s11307-021-01641-w.

M. Boktor, 等. Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS). Sci. Rep., 2022. 1210296DOI:10.1038/s41598-022-14042-yhttp://doi.org/10.1038/s41598-022-14042-y.

N. Kaza, A. Ojaghi, F. E. Robles. Virtual staining, segmentation, and classification of blood smears for label-free hematology analysis. BME Front., 2022. 20229853606DOI:10.34133/2022/9853606http://doi.org/10.34133/2022/9853606.

S. Soltani, 等. Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains. Sci. Rep., 2022. 129329DOI:10.1038/s41598-022-13332-9http://doi.org/10.1038/s41598-022-13332-9.

S. Soltani, 等. Deep UV microscopy identifies prostatic basal cells: an important biomarker for prostate cancer diagnostics. BME Front., 2022. 20229847962DOI:10.34133/2022/9847962http://doi.org/10.34133/2022/9847962.

L. Kang, 等. Deep learning enables ultraviolet photoacoustic microscopy-based histological imaging with near real-time virtual staining. Photoacoustics, 2022. 25100308DOI:10.1016/j.pacs.2021.100308http://doi.org/10.1016/j.pacs.2021.100308.

Cao, R. et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nature Biomedical Engineering 1-11, https://www.nature.com/articles/s41551-022-00940-zhttps://www.nature.com/articles/s41551-022-00940-z (2022).

D. Mayerich, 等. Stain-less staining for computed histopathology. Technology, 2015. 327-31. DOI:10.1142/S2339547815200010http://doi.org/10.1142/S2339547815200010.

Bayramoglu, N. et al. Towards virtual H & E staining of hyperspectral lung histology images using conditional generative adversarial networks. Proceedings of 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 64–71 (IEEE, Venice, Italy, 2017).

Gadermayr, M. et al. Which way round? A study on the performance of stain-translation for segmenting arbitrarily dyed histological images. Proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. 165–173 (Springer, Granada, Spain, 2018).

J. J. Levy, 等. A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies. Mod. Pathol., 2021. 34808-822. DOI:10.1038/s41379-020-00718-1http://doi.org/10.1038/s41379-020-00718-1.

Y. Y. Lin, 等. Unpaired multi-domain stain transfer for kidney histopathological images. Proc. AAAI Conf. Artif. Intell., 2022. 361630-1637. .

S. T. Liu, 等. Unpaired stain transfer using pathology-consistent constrained generative adversarial networks. IEEE Trans. Med. Imaging, 2021. 401977-1989. DOI:10.1109/TMI.2021.3069874http://doi.org/10.1109/TMI.2021.3069874.

W. S. Xie, 等. Prostate cancer risk stratification via nondestructive 3D pathology with deep learning-assisted gland analysis. Cancer Res., 2022. 82334-345. DOI:10.1158/0008-5472.CAN-21-2843http://doi.org/10.1158/0008-5472.CAN-21-2843.

Liu, S. J. et al. BCI: breast cancer immunohistochemical image generation through pyramid Pix2pix. Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1814–1823 (IEEE, New Orleans, 2022).

R. R. Zhang, 等. MVFStain: multiple virtual functional stain histopathology images generation based on specific domain mapping. Med. Image Anal., 2022. 80102520DOI:10.1016/j.media.2022.102520http://doi.org/10.1016/j.media.2022.102520.

Mercan, C. et al. Virtual staining for mitosis detection in breast histopathology. Proceedings of 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 1770–1774 (IEEE, Iowa City, IA, USA, 2020).

Levy, J. et al. Preliminary evaluation of the utility of deep generative histopathology image translation at a mid-sized NCI cancer center. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies. 302–311 (SCITEPRESS, Valletta, Malta, 2020).

A. Lahiani, 等. Seamless virtual whole slide image synthesis and validation using perceptual embedding consistency. IEEE J. Biomed. Health Inform., 2021. 25403-411. DOI:10.1109/JBHI.2020.2975151http://doi.org/10.1109/JBHI.2020.2975151.

Y. Y. Hong, 等. Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio. Sci. Rep., 2021. 1119255DOI:10.1038/s41598-021-98857-1http://doi.org/10.1038/s41598-021-98857-1.

Im, K. et al. An introduction to performing immunofluorescence staining. in Biobanking: Methods and Protocols (ed Yong, W. H. ), 299–311, https://doi.org/10.1007/978-1-4939-8935-5_26https://doi.org/10.1007/978-1-4939-8935-5_26 (Humana Press, New York, 2019).

P. Ghahremani, 等. Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification. Nat. Mach. Intell., 2022. 4401-412. DOI:10.1038/s42256-022-00471-xhttp://doi.org/10.1038/s42256-022-00471-x.

E. A. Burlingame, 等. SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning. Sci. Rep., 2020. 1017507DOI:10.1038/s41598-020-74500-3http://doi.org/10.1038/s41598-020-74500-3.

Z. H. Chen, 等. Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging. Biomed. Opt. Express, 2021. 125920-5938. DOI:10.1364/BOE.433597http://doi.org/10.1364/BOE.433597.

K. Stacke, 等. Measuring domain shift for deep learning in histopathology. IEEE J. Biomed. Health Inform., 2021. 25325-336. DOI:10.1109/JBHI.2020.3032060http://doi.org/10.1109/JBHI.2020.3032060.

Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 249–256 (JMLR. org, Sardinia, Italy, 2010).

Salehi, P. & Chalechale, A. Pix2Pix-based stain-to-stain translation: a solution for robust stain normalization in histopathology images analysis. Proceedings of 2020 International Conference on Machine Vision and Image Processing (MVIP), 1–7 (IEEE, Iran, 2020).

Y. Rivenson, 等. Deep learning enhanced mobile-phone microscopy. ACS Photonics, 2018. 52354-2364. DOI:10.1021/acsphotonics.8b00146http://doi.org/10.1021/acsphotonics.8b00146.

Lowe, D. G. Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision. 1150-1157 (IEEE, Kerkyra, Greece, 1999).

M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981. 24381-395. DOI:10.1145/358669.358692http://doi.org/10.1145/358669.358692.

H. Bay, 等. Speeded-up robust features (SURF). Comput. Vis. Image Underst., 2008. 110346-359. DOI:10.1016/j.cviu.2007.09.014http://doi.org/10.1016/j.cviu.2007.09.014.

N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, Cybern., 1979. 962-66. DOI:10.1109/TSMC.1979.4310076http://doi.org/10.1109/TSMC.1979.4310076.

Y. J. Zhang, 等. Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks. Intell. Comput., 2022. 20229818965DOI:10.34133/2022/9818965http://doi.org/10.34133/2022/9818965.

M. Ashraf, 等. A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network. Sci. Rep., 2022. 121392DOI:10.1038/s41598-022-05001-8http://doi.org/10.1038/s41598-022-05001-8.

M. J. Fanous, G. Popescu. GANscan: continuous scanning microscopy using deep learning deblurring. Light Sci. Appl., 2022. 11265DOI:10.1038/s41377-022-00952-zhttp://doi.org/10.1038/s41377-022-00952-z.

E. Reinhard, 等. Color transfer between images. IEEE Comput. Graph. Appl., 2001. 2134-41. DOI:10.1109/38.946629http://doi.org/10.1109/38.946629.

Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. Proceedings of 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 1107–1110 (IEEE, Boston, MA, USA, 2009).

A. Vahadane, 等. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging, 2016. 351962-1971. DOI:10.1109/TMI.2016.2529665http://doi.org/10.1109/TMI.2016.2529665.

Shrivastava, A. et al. Self-attentive adversarial stain normalization. Proceedings of the International Conference on Pattern Recognition. 120–140, https://doi.org/10.1007/978-3-030-68763-2_10https://doi.org/10.1007/978-3-030-68763-2_10 (Springer, 2021).

A. Janowczyk, A. Basavanhally, A. Madabhushi. Stain normalization using sparse AutoEncoders (StaNoSA): application to digital pathology. Comput. Med. Imaging Graph., 2017. 5750-61. DOI:10.1016/j.compmedimag.2016.05.003http://doi.org/10.1016/j.compmedimag.2016.05.003.

Goodfellow, I. et al. Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems. 2672–2680 (MIT Press, Montreal, Canada, 2014).

T. R. Liu, 等. Deep learning-based holographic polarization microscopy. ACS Photonics, 2020. 73023-3034. DOI:10.1021/acsphotonics.0c01051http://doi.org/10.1021/acsphotonics.0c01051.

Yang, X. L. et al. Virtual stain transfer in histology via cascaded deep neural networks. ACS Photonics 9, 3134–3143, https://doi.org/10.1021/acsphotonics.2c00932https://doi.org/10.1021/acsphotonics.2c00932 (2022).

Isola, P., et al. Image-to-image translation with conditional adversarial networks. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5967–5976, https://doi.org/10.1109/CVPR.2017.632https://doi.org/10.1109/CVPR.2017.632 (IEEE, Honolulu, HI, USA, 2017).

Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015- 18th International Conference.: (Springer, Munich, Germany, 2015).

Z. Wang, 等. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 2004. 13600-612. DOI:10.1109/TIP.2003.819861http://doi.org/10.1109/TIP.2003.819861.

A. Chambolle. An algorithm for total variation minimization and applications. J. Math. Imaging Vis., 2004. 2089-97. DOI:10.1023/B:JMIV.0000011321.19549.88http://doi.org/10.1023/B:JMIV.0000011321.19549.88.

Q. Huynh-Thu, M. Ghanbari. Scope of validity of PSNR in image/video quality assessment. Electron. Lett., 2008. 44800-801. DOI:10.1049/el:20080522http://doi.org/10.1049/el:20080522.

Wang, Z., Simoncelli, E. P. & Bovik, A. C. Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. 1398–1402 (IEEE, Pacific Grove, CA, USA, 2003).

Heusel, M. et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Proceedings of the 31st International Conference on Neural Information Processing Systems. 6629–6640 (Curran Associates Inc., Long Beach, USA, 2017).

Salimans, T. et al. Improved techniques for training GANs. Proceedings of the 30th International Conference on Neural Information Processing Systems. 2234–2242 (Curran Associates Inc., Barcelona, Spain, 2016).

G. Landini, G. Martinelli, F. Piccinini. Colour deconvolution: stain unmixing in histological imaging. Bioinformatics, 2021. 371485-1487. DOI:10.1093/bioinformatics/btaa847http://doi.org/10.1093/bioinformatics/btaa847.

K. A. Tran, 等. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med., 2021. 13152DOI:10.1186/s13073-021-00968-xhttp://doi.org/10.1186/s13073-021-00968-x.

Turnaround Time (TAT) - Biopsies. https://documents.cap.org/documents/cap-22-turnaround-time-biopsies-2021.pdfhttps://documents.cap.org/documents/cap-22-turnaround-time-biopsies-2021.pdf (2021).

Y. Gu, 等. Transfer recurrent feature learning for endomicroscopy image recognition. IEEE Trans. Med. Imaging, 2019. 38791-801. DOI:10.1109/TMI.2018.2872473http://doi.org/10.1109/TMI.2018.2872473.

Y. Yagi. Color standardization and optimization in Whole Slide Imaging. Diagn. Pathol., 2011. 6S15DOI:10.1186/1746-1596-6-S1-S15http://doi.org/10.1186/1746-1596-6-S1-S15.

K. Tomczak, P. Czerwińska, M. Wiznerowicz. Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol., 2015. 19A68-A77. DOI:10.5114/wo.2014.47136http://doi.org/10.5114/wo.2014.47136.

P. Bándi, 等. From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge. IEEE Trans. Med. Imaging, 2019. 38550-560. DOI:10.1109/TMI.2018.2867350http://doi.org/10.1109/TMI.2018.2867350.

W. Bulten, 等. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat. Med., 2022. 28154-163. DOI:10.1038/s41591-021-01620-2http://doi.org/10.1038/s41591-021-01620-2.

M. Aubreville, 等. Mitosis domain generalization in histopathology images - The MIDOG challenge. Med. Image Anal., 2022. 84102699DOI:10.1016/j.media.2022.102699http://doi.org/10.1016/j.media.2022.102699.

Liu, Z. et al. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. 9992–10002. (IEEE, Montreal, QC, Canada, 2021).

Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. Print at https://arxiv.org/abs/2010.11929https://arxiv.org/abs/2010.11929 (2021).

Chen, R. J. et al. Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16123–16134, https://doi.org/10.1109/CVPR52688.2022.01567https://doi.org/10.1109/CVPR52688.2022.01567 (IEEE, New Orleans, LA, USA, 2022).

E. Gómez-de-Mariscal, 等. DeepImageJ: a user-friendly environment to run deep learning models in ImageJ. Nat. Methods, 2021. 181192-1195. DOI:10.1038/s41592-021-01262-9http://doi.org/10.1038/s41592-021-01262-9.

D. R. Stirling, 等. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinforma., 2021. 22433DOI:10.1186/s12859-021-04344-9http://doi.org/10.1186/s12859-021-04344-9.

P. Bankhead, 等. QuPath: open source software for digital pathology image analysis. Sci. Rep., 2017. 716878DOI:10.1038/s41598-017-17204-5http://doi.org/10.1038/s41598-017-17204-5.

Rivenson, Y. et al. PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning. Print at https://arxiv.org/abs/1807.07701https://arxiv.org/abs/1807.07701 (2018).

Bai, B. J. et al. Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning. Print at https://arxiv.org/abs/2112.05240https://arxiv.org/abs/2112.05240 (2021).

de Haan K. et al. Deep learning-based transformation of the H & E stain into special stains. Print at https://arxiv.org/abs/2008.08871https://arxiv.org/abs/2008.08871 (2020).

N. Bouteldja, 等. Improving unsupervised stain-to-stain translation using self-supervision and meta-learning. J. Pathol. Inform., 2022. 13100107DOI:10.1016/j.jpi.2022.100107http://doi.org/10.1016/j.jpi.2022.100107.

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰