1.Pictor Labs, Inc., Los Angeles, USA
2.Electrical and Computer Engineering Department, University of California, Los Angeles 90095 CA, USA
3.Bioengineering Department, University of California, Los Angeles 90095 CA, USA
4.California NanoSystems Institute (CNSI), University of California, Los Angeles 90095 CA, USA
5.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles 90095 CA, USA
Yair Rivenson (rivenson@pictorlabs.ai)
纸质出版日期:2022-11-30,
网络出版日期:2022-10-14,
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Deep learning accelerates whole slide imaging for next-generation digital pathology applications[J]. LSA, 2022,11(11):2558-2560.
Rivenson, Y. & Ozcan, A. Deep learning accelerates whole slide imaging for next-generation digital pathology applications. Light: Science & Applications, 11, 2558-2560 (2022).
Deep learning accelerates whole slide imaging for next-generation digital pathology applications[J]. LSA, 2022,11(11):2558-2560. DOI: 10.1038/s41377-022-00999-y.
Rivenson, Y. & Ozcan, A. Deep learning accelerates whole slide imaging for next-generation digital pathology applications. Light: Science & Applications, 11, 2558-2560 (2022). DOI: 10.1038/s41377-022-00999-y.
Deep learning demonstrates the ability to significantly increase the scanning speed of whole slide imaging in histology. This transformative solution can be used to further accelerate the adoption of digital pathology.
M. D. Zarella, 等. A practical guide to whole slide imaging: a white paper from the Digital Pathology Association. Arch. Pathol. Lab. Med., 2019. 143222DOI:10.5858/arpa.2018-0343-RAhttp://doi.org/10.5858/arpa.2018-0343-RA.
S. W. Jahn, M. Plass, F. Moinfar. Digital pathology: advantages, limitations and emerging perspectives. J. Clin. Med., 2020. 93697DOI:10.3390/jcm9113697http://doi.org/10.3390/jcm9113697.
DPA. FDA Guidance for WSI. https://digitalpathologyassociation.org/fda-guidance-for-whole-slide-imaginghttps://digitalpathologyassociation.org/fda-guidance-for-whole-slide-imaging (2018).
S. Rajaganesan, 等. Comparative assessment of digital pathology systems for primary diagnosis. J. Pathol. Inform., 2021. 1225DOI:10.4103/jpi.jpi_94_20http://doi.org/10.4103/jpi.jpi_94_20.
M. G. L. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc., 2000. 19882-87. DOI:10.1046/j.1365-2818.2000.00710.xhttp://doi.org/10.1046/j.1365-2818.2000.00710.x.
J. M. Rodenburg, H. M. L. Faulkner. A phase retrieval algorithm for shifting illumination. Appl. Phys. Lett., 2004. 854795-4797. DOI:10.1063/1.1823034http://doi.org/10.1063/1.1823034.
G. A. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 2013. 7739-745. DOI:10.1038/nphoton.2013.187http://doi.org/10.1038/nphoton.2013.187.
W. Luo, 等. Synthetic aperture-based on-chip microscopy. Light Sci. Appl., 2015. 4e261DOI:10.1038/lsa.2015.34http://doi.org/10.1038/lsa.2015.34.
L. Tian, 等. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope. Biomed. Opt. Express, 2014. 52376-2389. DOI:10.1364/BOE.5.002376http://doi.org/10.1364/BOE.5.002376.
Y. Rivenson, 等. Deep learning microscopy. Optica, 2017. 41437-1443. DOI:10.1364/OPTICA.4.001437http://doi.org/10.1364/OPTICA.4.001437.
Y. L. Luo, 等. Single-shot autofocusing of microscopy images using deep learning. ACS Photonics, 2021. 8625-638. DOI:10.1021/acsphotonics.0c01774http://doi.org/10.1021/acsphotonics.0c01774.
Zhang, Y. J. et al. Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks. Preprint at https://doi.org/10.48550/arXiv.2207.02946https://doi.org/10.48550/arXiv.2207.02946 (2022).
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.
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.
M. Nederlof, 等. High-throughput profiling of tissue and tissue model microarrays: combined transmitted light and 3-color fluorescence digital pathology. J. Pathol. Inform., 2011. 250DOI:10.4103/2153-3539.89849http://doi.org/10.4103/2153-3539.89849.
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.
Isola, P. et al. Image-to-image translation with conditional adversarial networks. In: Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition. 5967–5976 (Honolulu, HI, USA, IEEE, 2017).
Y. X. Pang, 等. Image-to-image translation: methods and applications. IEEE Trans. Multimed., 2022. 243859-3881. DOI:10.1109/TMM.2021.3109419http://doi.org/10.1109/TMM.2021.3109419.
I. J. Goodfellow, 等. Generative adversarial networks. Commun. ACM, 2020. 63139-144. DOI:10.1145/3422622http://doi.org/10.1145/3422622.
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