Emerging role of deep learning-based artificial intelligence in tumor pathology

Yahui Jiang, Meng Yang, Shuhao Wang, Xiangchun Li, Yan Sun, Yahui Jiang, Meng Yang, Shuhao Wang, Xiangchun Li, Yan Sun

Abstract

The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

Keywords: artificial intelligence; artificial intelligence-assisted bioinformatic analysis; deep learning; pathology; tumor.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2020 The Authors. Cancer Communications published by John Wiley & Sons Australia, Ltd. on behalf of Sun Yat-sen University Cancer Center.

Figures

FIGURE 1
FIGURE 1
General workflow of deep learning‐based artificial intelligence in pathology. First, tissue slides are transformed into whole‐slide images (WSIs) through digital scanners. Next, various neural networks learn and extract features from the images patch‐to‐patch. Finally, features are selected and classified to construct different diagnosis‐ or prognosis‐ models

References

    1. McCarthy J, Minsky ML, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence ‐ August 31, 1955. Ai Mag. 2006;27(4):12–4.
    1. Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol. 2018;11(9):1555–61. 10.18240/ijo.2018.09.21.
    1. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist‐level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8. 10.1038/nature21056.
    1. Prewitt JM, Mendelsohn ML. The analysis of cell images. Ann N Y Acad Sci. 1966;128(3):1035–53. 10.1111/j.1749-6632.1965.tb11715.x.
    1. Tomczak K, Czerwinska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68–77. 10.5114/wo.2014.47136.
    1. Gutman DA, Cobb J, Somanna D, Park Y, Wang F, Kurc T, et al. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J Am Med Inform Assoc. 2013;20(6):1091–98. 10.1136/amiajnl-2012-001469.
    1. Liu Y, Sun Y, Broaddus R, Liu J, Sood AK, Shmulevich I, et al. Integrated analysis of gene expression and tumor nuclear image profiles associated with chemotherapy response in serous ovarian carcinoma. PLoS One. 2012;7(5):e36383 10.1371/journal.pone.0036383.
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. 10.1038/nature14539.
    1. Jain RK, Mehta R, Dimitrov R, Larsson LG, Musto PM, Hodges KB, et al. Atypical ductal hyperplasia: interobserver and intraobserver variability. Mod Pathol. 2011;24(7):917–923. 10.1038/modpathol.2011.66.
    1. Fuchs TJ, Wild PJ, Moch H, Buhmann JM. Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. Med Image Comput Comput Assist Interv. 2008;11(Pt 2):1–8. 10.1007/978-3-540-85990-1_1.
    1. Proscia. Proscia digital pathology. . 2019. Available from: .
    1. Deep Lens . Digital pathology cloud platform. . 2019. Available from: .
    1. PathAI. PathAI. . 2019. Available from: .
    1. Aifora. WebMicroscope. Big pictures. Deep Diagnosis. . 2019. Available from: .
    1. Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB, Contis L, et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch Pathol Lab Med. 2013;137(12):1710–1722. 10.5858/arpa.2013-0093-CP.
    1. Fraggetta F, Garozzo S, Zannoni GF, Pantanowitz L, Rossi ED. Routine Digital Pathology Workflow: The Catania Experience. J Pathol Inform. 2017;8:51 10.4103/jpi.jpi_58_17.
    1. Cheng CL, Azhar R, Sng SH, Chua YQ, Hwang JS, Chin JP, et al. Enabling digital pathology in the diagnostic setting: navigating through the implementation journey in an academic medical centre. J Clin Pathol. 2016;69(9):784–792. 10.1136/jclinpath-2015-203600.
    1. Araujo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One. 2017;12(6):e0177544 10.1371/journal.pone.0177544.
    1. Bejnordi BE, Zuidhof G, Balkenhol M, Hermsen M, Bult P, van Ginneken B, et al. Context‐aware stacked convolutional neural networks for classification of breast carcinomas in whole‐slide histopathology images. J Med Imaging (Bellingham). 2017;4(4):044504 10.1117/1.JMI.4.4.044504.
    1. Ehteshami Bejnordi B, Mullooly M, Pfeiffer RM, Fan S, Vacek PM, Weaver DL, et al. Using deep convolutional neural networks to identify and classify tumor‐associated stroma in diagnostic breast biopsies. Mod Pathol. 2018;31(10):1502–1512. 10.1038/s41379-018-0073-z.
    1. Kainz P, Pfeiffer M, Urschler M. Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. PeerJ. 2017;5:e3874 10.7717/peerj.3874.
    1. Awan R, Sirinukunwattana K, Epstein D, Jefferyes S, Qidwai U, Aftab Z, et al. Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images. Sci Rep. 2017;7(1):16852 10.1038/s41598-017-16516-w.
    1. Wang L, Ding L, Liu Z, Sun L, Chen L, Jia R, et al. Automated identification of malignancy in whole‐slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. Br J Ophthalmol. 2019. 10.1136/bjophthalmol-2018-313706.
    1. Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG. Multi‐Instance Multi‐Label Learning for Multi‐Class Classification of Whole Slide Breast Histopathology Images. IEEE Trans Med Imaging. 2018;37(1):316–325. 10.1109/TMI.2017.2758580.
    1. Wang S, Zhu Y, Yu L, Chen H, Lin H, Wan X, et al. RMDL: Recalibrated multi‐instance deep learning for whole slide gastric image classification. Med Image Anal. 2019;58:101549 10.1016/j.media.2019.101549.
    1. Tomita N, Abdollahi B, Wei J, Ren B, Suriawinata A, Hassanpour S. Attention‐Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides. JAMA Netw Open. 2019;2(11):e1914645 10.1001/jamanetworkopen.2019.14645.
    1. Zhang L, Le L, Nogues I, Summers RM, Liu S, Yao J. DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE J Biomed Health Inform. 2017;21(6):1633–1643. 10.1109/JBHI.2017.2705583.
    1. Vaickus LJ, Suriawinata AA, Wei JW, Liu X. Automating the Paris System for urine cytopathology‐A hybrid deep‐learning and morphometric approach. Cancer Cytopathol. 2019;127(2):98–115. 10.1002/cncy.22099.
    1. Sanghvi AB, Allen EZ, Callenberg KM, Pantanowitz L. Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 2019;127(10):658–666. 10.1002/cncy.22176.
    1. Guan Q, Wang Y, Ping B, Li D, Du J, Qin Y, et al. Deep convolutional neural network VGG‐16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer. 2019;10(20):4876–4882. 10.7150/jca.28769.
    1. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyo D, et al. Classification and mutation prediction from non‐small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559–1567. 10.1038/s41591-018-0177-5.
    1. Gertych A, Swiderska‐Chadaj Z, Ma Z, Ing N, Markiewicz T, Cierniak S, et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep. 2019;9(1):1483 10.1038/s41598-018-37638-9.
    1. Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist‐level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep. 2019;9(1):3358 10.1038/s41598-019-40041-7.
    1. Korbar B, Olofson AM, Miraflor AP, Nicka CM, Suriawinata MA, Torresani L, et al. Deep Learning for Classification of Colorectal Polyps on Whole‐slide Images. J Pathol Inform. 2017;8:30 10.4103/jpi.jpi_34_17.
    1. Wu M, Yan C, Liu H, Liu Q. Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Biosci Rep. 2018;38(3). 10.1042/BSR20180289.
    1. Wang Y, Guan Q, Lao I, Wang L, Wu Y, Li D et al. Using deep convolutional neural networks for multi‐classification of thyroid tumor by histopathology: a large‐scale pilot study. Ann Transl Med. 2019;7(18):468 10.21037/atm.2019.08.54.
    1. Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE‐ResNet module. PLoS One. 2019;14(3):e0214587 10.1371/journal.pone.0214587.
    1. Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Biosci Rep. 2018;38(6). 10.1042/BSR20181769.
    1. Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H. Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks. Biomed Res Int. 2017;2017:4067832 10.1155/2017/4067832.
    1. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018;8(1):12054 10.1038/s41598-018-30535-1.
    1. Ertosun MG, Rubin DL. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. AMIA Annu Symp Proc. 2015;2015:1899–1908.
    1. Wan T, Cao JJ, Chen JH, Qin ZC. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi‐level image features. Neurocomputing. 2017;229:34–44. 10.1016/j.neucom.2016.05.084.
    1. Mishra R, Daescu O, Leavey P, Rakheja D, Sengupta A. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma. J Comput Biol. 2018;25(3):313–325. 10.1089/cmb.2017.0153.
    1. Cruz‐Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, et al. Accurate and reproducible invasive breast cancer detection in whole‐slide images: A Deep Learning approach for quantifying tumor extent. Sci Rep. 2017;7:46450 10.1038/srep46450.
    1. Cruz‐Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih N, et al. High‐throughput adaptive sampling for whole‐slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS One. 2018;13(5):e0196828 10.1371/journal.pone.0196828.
    1. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199–2210. 10.1001/jama.2017.14585.
    1. Liu Y, Kohlberger T, Norouzi M, Dahl GE, Smith JL, Mohtashamian A, et al. Artificial Intelligence‐Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. Arch Pathol Lab Med. 2019;143(7):859–868. 10.5858/arpa.2018-0147-OA.
    1. Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol. 2018;42(12):1636–1646. 10.1097/PAS.0000000000001151.
    1. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004;351(8):781–791. 10.1056/NEJMoa040766.
    1. Cohen SJ, Punt CJ, Iannotti N, Saidman BH, Sabbath KD, Gabrail NY, et al. Relationship of circulating tumor cells to tumor response, progression‐free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(19):3213–3221. 10.1200/JCO.2007.15.8923.
    1. de Bono JS, Scher HI, Montgomery RB, Parker C, Miller MC, Tissing H, et al. Circulating tumor cells predict survival benefit from treatment in metastatic castration‐resistant prostate cancer. Clin Cancer Res. 2008;14(19):6302–6309. 10.1158/1078-0432.CCR-08-0872.
    1. Rhim AD, Mirek ET, Aiello NM, Maitra A, Bailey JM, McAllister F, et al. EMT and dissemination precede pancreatic tumor formation. Cell. 2012;148(1‐2):349–361. 10.1016/j.cell.2011.11.025.
    1. Chaffer CL, Weinberg RA. A perspective on cancer cell metastasis. Science. 2011;331(6024):1559–1564. 10.1126/science.1203543.
    1. Pantel K, Alix‐Panabieres C. Real‐time liquid biopsy in cancer patients: fact or fiction? Cancer Res. 2013;73(21):6384–6388. 10.1158/0008-5472.CAN-13-2030.
    1. Strati A, Kasimir‐Bauer S, Markou A, Parisi C, Lianidou ES. Comparison of three molecular assays for the detection and molecular characterization of circulating tumor cells in breast cancer. Breast Cancer Res. 2013;15(2):R20 10.1186/bcr3395.
    1. Zeune LL, de Wit S, Berghuis AMS, IJzerman, MJ , Terstappen L, Brune C. How to Agree on a CTC: Evaluating the Consensus in Circulating Tumor Cell Scoring. Cytometry A. 2018;93(12):1202–1206. 10.1002/cyto.a.23576.
    1. Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz‐Roa A, et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal. 2015;20(1):237–248. 10.1016/j.media.2014.11.010.
    1. Veta M, Heng YJ, Stathonikos N, Bejnordi BE, Beca F, Wollmann T, et al. Predicting breast tumor proliferation from whole‐slide images: The TUPAC16 challenge. Med Image Anal. 2019;54:111–121. 10.1016/j.media.2019.02.012.
    1. Weis CA, Kather JN, Melchers S, Al‐Ahmdi H, Pollheimer MJ, Langner C, et al. Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome. Diagn Pathol. 2018;13(1):64 10.1186/s13000-018-0739-3.
    1. Halama N, Michel S, Kloor M, Zoernig I, Benner A, Spille A, et al. Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. Cancer Res. 2011;71(17):5670–5677. 10.1158/0008-5472.CAN-11-0268.
    1. Savas P, Salgado R, Denkert C, Sotiriou C, Darcy PK, Smyth MJ, et al. Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol. 2016;13(4):228–241. 10.1038/nrclinonc.2015.215.
    1. Turkki R, Linder N, Kovanen PE, Pellinen T, Lundin J. Antibody‐supervised deep learning for quantification of tumor‐infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform. 2016;7:38 10.4103/2153-3539.189703.
    1. Aprupe L, Litjens G, Brinker TJ, van der Laak J, Grabe N. Robust and accurate quantification of biomarkers of immune cells in lung cancer micro‐environment using deep convolutional neural networks. PeerJ. 2019;7:e6335 10.7717/peerj.6335.
    1. Saha M, Chakraborty C, Arun I, Ahmed R, Chatterjee S. An Advanced Deep Learning Approach for Ki‐67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer. Sci Rep. 2017;7(1):3213 10.1038/s41598-017-03405-5.
    1. Niazi MKK, Tavolara TE, Arole V, Hartman DJ, Pantanowitz L, Gurcan MN. Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning. PLoS One. 2018;13(4):e0195621 10.1371/journal.pone.0195621.
    1. Vandenberghe ME, Scott ML, Scorer PW, Soderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep. 2017;7:45938 10.1038/srep45938.
    1. Khameneh FD, Razavi S, Kamasak M. Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Comput Biol Med. 2019;110:164–174. 10.1016/j.compbiomed.2019.05.020.
    1. Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph. 2017;61:2–13. 10.1016/j.compmedimag.2017.06.001.
    1. Sha L, Osinski BL, Ho IY, Tan TL, Willis C, Weiss H, et al. Multi‐Field‐of‐View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death‐Ligand 1 Status from Whole‐Slide Hematoxylin and Eosin Images. J Pathol Inform. 2019;10:24 10.4103/jpi.jpi_24_19.
    1. Schaumberg AJ, Rubin MA, Fuchs TJ. H&E‐stained whole slide deep learning predicts SPOP mutation state in prostate cancer. 2018. Available from:
    1. An J, Wang C, Deng Y, Yu L, Huang H. Destruction of full‐length androgen receptor by wild‐type SPOP, but not prostate‐cancer‐associated mutants. Cell Rep. 2014;6(4):657–669. 10.1016/j.celrep.2014.01.013.
    1. Barbieri CE, Baca SC, Lawrence MS, Demichelis F, Blattner M, Theurillat JP, et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat Genet. 2012;44(6):685–689. 10.1038/ng.2279.
    1. Kather JN, Pearson AT, Halama N, Jager D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054–1056. 10.1038/s41591-019-0462-y.
    1. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018;8(1):3395 10.1038/s41598-018-21758-3.
    1. Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 2019;16(1):e1002730 10.1371/journal.pmed.1002730.
    1. Wang S, Chen A, Yang L, Cai L, Xie Y, Fujimoto J, et al. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep. 2018;8(1):10393 10.1038/s41598-018-27707-4.
    1. Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz‐Sloan JS, Velazquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018;115(13):E2970–E9. 10.1073/pnas.1717139115.
    1. Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30 10.1038/s41523-018-0079-1.
    1. Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. 2018;27:317–328. 10.1016/j.ebiom.2017.12.026.
    1. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross‐sectional study. PLoS Med. 2018;15(11):e1002683 10.1371/journal.pmed.1002683.
    1. Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med Image Anal. 2016;33:170–175. 10.1016/j.media.2016.06.037.
    1. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence. 2019;1(5):206–215. 10.1038/s42256-019-0048-x.
    1. Ching T, Himmelstein DS, Beaulieu‐Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141). 10.1098/rsif.2017.0387.
    1. Wang X, Janowczyk A, Zhou Y, Thawani R, Fu P, Schalper K, et al. Prediction of recurrence in early stage non‐small cell lung cancer using computer extracted nuclear features from digital H&E images. Sci Rep. 2017;7(1):13543 10.1038/s41598-017-13773-7.
    1. FDA . Developing a software precertification program: a working model. . 2019. Available from: .
    1. Daniel, G. , Silcox, C. , Sharma, I. & Wright, M. Current state and near‐term priorities for AI‐enabled diagnostic support software in health care. . 2019. Available from: .
    1. Pesapane F, Volonte C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745–753. 10.1007/s13244-018-0645-y.
    1. Food and Drug Administration . IntelliSite Pathology Solution (PIPS, Philips Medical Systems). . 2017. Available from: .
    1. PAIGE. PAIGE. . 2019. Available from: .
    1. Business Wire . FDA grants breakthrough designation to . . 2019. Available from: .
    1. Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2018;9:38 10.4103/jpi.jpi_53_18.
    1. Bueno G, Fernandez‐Carrobles MM, Deniz O, Garcia‐Rojo M. New Trends of Emerging Technologies in Digital Pathology. Pathobiology. 2016;83(2‐3):61–69. 10.1159/000443482.
    1. Montalto MC. An industry perspective: An update on the adoption of whole slide imaging. J Pathol Inform. 2016;7:18 10.4103/2153-3539.180014.
    1. Lowe, A. , et al. Validation of digital pathology in a healthcare environment. . 2011. Available from: .
    1. Digital Pathology Association . Healthcare FAQs. . 2019. Available from: .
    1. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology ‐ new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703–715. 10.1038/s41571-019-0252-y.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren