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For: Djuric U, Zadeh G, Aldape K, Diamandis P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. NPJ Precis Oncol. 2017;1:22. [PMID: 29872706 DOI: 10.1038/s41698-017-0022-1] [Cited by in Crossref: 74] [Cited by in F6Publishing: 65] [Article Influence: 14.8] [Reference Citation Analysis]
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2 Chaddad A, Kucharczyk MJ, Daniel P, Sabri S, Jean-Claude BJ, Niazi T, Abdulkarim B. Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation. Front Oncol 2019;9:374. [PMID: 31165039 DOI: 10.3389/fonc.2019.00374] [Cited by in Crossref: 55] [Cited by in F6Publishing: 46] [Article Influence: 18.3] [Reference Citation Analysis]
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5 Mingo J, Luna S, Gaafar A, Nunes-Xavier CE, Torices L, Mosteiro L, Ruiz R, Guerra I, Llarena R, Angulo JC, López JI, Pulido R. Precise definition of PTEN C-terminal epitopes and its implications in clinical oncology. NPJ Precis Oncol 2019;3:11. [PMID: 30993208 DOI: 10.1038/s41698-019-0083-4] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.7] [Reference Citation Analysis]
6 Jang HJ, Song IH, Lee SH. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers (Basel) 2021;13:3811. [PMID: 34359712 DOI: 10.3390/cancers13153811] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Jones AD, Graff JP, Darrow M, Borowsky A, Olson KA, Gandour-Edwards R, Datta Mitra A, Wei D, Gao G, Durbin-Johnson B, Rashidi HH. Impact of pre-analytical variables on deep learning accuracy in histopathology. Histopathology. 2019;75:39-53. [PMID: 30801768 DOI: 10.1111/his.13844] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
8 Sarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R, Diamandis P. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med 2019;2:28. [PMID: 31304375 DOI: 10.1038/s41746-019-0106-0] [Cited by in Crossref: 37] [Cited by in F6Publishing: 32] [Article Influence: 12.3] [Reference Citation Analysis]
9 Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. Acta Neurochir Suppl 2022;134:125-38. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Reference Citation Analysis]
10 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Calderaro J, Ziol M, Paradis V, Zucman-rossi J. Molecular and histological correlations in liver cancer. Journal of Hepatology 2019;71:616-30. [DOI: 10.1016/j.jhep.2019.06.001] [Cited by in Crossref: 74] [Cited by in F6Publishing: 73] [Article Influence: 24.7] [Reference Citation Analysis]
12 Tabibu S, Vinod PK, Jawahar CV. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep 2019;9:10509. [PMID: 31324828 DOI: 10.1038/s41598-019-46718-3] [Cited by in Crossref: 36] [Cited by in F6Publishing: 25] [Article Influence: 12.0] [Reference Citation Analysis]
13 Ye JJ. Construction and Utilization of a Neural Network Model to Predict Current Procedural Terminology Codes from Pathology Report Texts. J Pathol Inform 2019;10:13. [PMID: 31057982 DOI: 10.4103/jpi.jpi_3_19] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
14 Kashyap A, Fomitcheva Khartchenko A, Pati P, Gabrani M, Schraml P, Kaigala GV. Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues. Nat Biomed Eng 2019;3:478-90. [DOI: 10.1038/s41551-019-0386-3] [Cited by in Crossref: 12] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
15 Zhang Z, Chen P, Mcgough M, Xing F, Wang C, Bui M, Xie Y, Sapkota M, Cui L, Dhillon J, Ahmad N, Khalil FK, Dickinson SI, Shi X, Liu F, Su H, Cai J, Yang L. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat Mach Intell 2019;1:236-45. [DOI: 10.1038/s42256-019-0052-1] [Cited by in Crossref: 68] [Cited by in F6Publishing: 18] [Article Influence: 22.7] [Reference Citation Analysis]
16 Loughrey MB, Bankhead P, Coleman HG, Hagan RS, Craig S, McCorry AMB, Gray RT, McQuaid S, Dunne PD, Hamilton PW, James JA, Salto-Tellez M. Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis. Histopathology 2018;73:327-38. [PMID: 29575153 DOI: 10.1111/his.13516] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 5.3] [Reference Citation Analysis]
17 Wood-Trageser MA, Lesniak AJ, Demetris AJ. Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring. Transplantation 2019;103:1306-22. [PMID: 30768568 DOI: 10.1097/TP.0000000000002656] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
18 Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2020;24:89-99. [PMID: 31908578 DOI: 10.4196/kjpp.2020.24.1.89] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
19 Hosein S, Reitblat CR, Cone EB, Trinh QD. Clinical applications of artificial intelligence in urologic oncology. Curr Opin Urol 2020;30:748-53. [PMID: 32941255 DOI: 10.1097/MOU.0000000000000819] [Reference Citation Analysis]
20 Jaber MI, Song B, Taylor C, Vaske CJ, Benz SC, Rabizadeh S, Soon-Shiong P, Szeto CW. A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res 2020;22:12. [PMID: 31992350 DOI: 10.1186/s13058-020-1248-3] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
21 Dov D, Kovalsky SZ, Assaad S, Cohen J, Range DE, Pendse AA, Henao R, Carin L. Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. Med Image Anal 2021;67:101814. [PMID: 33049578 DOI: 10.1016/j.media.2020.101814] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
22 Landau MS, Pantanowitz L. Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. Journal of the American Society of Cytopathology 2019;8:230-41. [DOI: 10.1016/j.jasc.2019.03.003] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 8.3] [Reference Citation Analysis]
23 Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021;12:42. [PMID: 34881097 DOI: 10.4103/jpi.jpi_36_21] [Reference Citation Analysis]
24 Voith von Voithenberg L, Fomitcheva Khartchenko A, Huber D, Schraml P, Kaigala GV. Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity. Nucleic Acids Res 2020;48:e17. [PMID: 31853536 DOI: 10.1093/nar/gkz1151] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
25 Fröhlich H, Balling R, Beerenwinkel N, Kohlbacher O, Kumar S, Lengauer T, Maathuis MH, Moreau Y, Murphy SA, Przytycka TM, Rebhan M, Röst H, Schuppert A, Schwab M, Spang R, Stekhoven D, Sun J, Weber A, Ziemek D, Zupan B. From hype to reality: data science enabling personalized medicine. BMC Med 2018;16:150. [PMID: 30145981 DOI: 10.1186/s12916-018-1122-7] [Cited by in Crossref: 115] [Cited by in F6Publishing: 81] [Article Influence: 28.8] [Reference Citation Analysis]
26 Czyzewski T, Daniel N, Rochman M, Caldwell JM, Osswald GA, Collins MH, Rothenberg ME, Savir Y. Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features. IEEE Open J Eng Med Biol 2021;2:218-23. [PMID: 34505063 DOI: 10.1109/ojemb.2021.3089552] [Reference Citation Analysis]
27 Wu LL, Wang JL, Huang W, Liu X, Huang YY, Zeng J, Cui CY, Lu JB, Lin P, Long H, Zhang LJ, Wei J, Lu Y, Ma GW. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis. Front Oncol 2021;11:565755. [PMID: 33912439 DOI: 10.3389/fonc.2021.565755] [Reference Citation Analysis]
28 Al Mouiee D, Meijering E, Kalloniatis M, Nivison-Smith L, Williams RA, Nayagam DAX, Spencer TC, Luu CD, McGowan C, Epp SB, Shivdasani MN. Classifying Retinal Degeneration in Histological Sections Using Deep Learning. Transl Vis Sci Technol 2021;10:9. [PMID: 34110385 DOI: 10.1167/tvst.10.7.9] [Reference Citation Analysis]
29 Tollemar V, Tudzarovski N, Boberg E, Törnqvist Andrén A, Al-adili A, Le Blanc K, Garming Legert K, Bottai M, Warfvinge G, Sugars R. Quantitative chromogenic immunohistochemical image analysis in cellprofiler software: Quantitative chromogenic immunohistochemistry. Cytometry 2018;93:1051-9. [DOI: 10.1002/cyto.a.23575] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
30 Shin SH, Bode AM, Dong Z. Addressing the challenges of applying precision oncology. NPJ Precis Oncol 2017;1:28. [PMID: 29872710 DOI: 10.1038/s41698-017-0032-z] [Cited by in Crossref: 15] [Cited by in F6Publishing: 17] [Article Influence: 3.0] [Reference Citation Analysis]
31 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Steiner DF, Chen PC, Mermel CH. Closing the translation gap: AI applications in digital pathology. Biochim Biophys Acta Rev Cancer 2021;1875:188452. [PMID: 33065195 DOI: 10.1016/j.bbcan.2020.188452] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Lovinfosse P, Hatt M, Visvikis D, Hustinx R. Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018;6:393-410. [DOI: 10.1007/s40336-018-0299-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
34 Marsh JN, Matlock MK, Kudose S, Liu TC, Stappenbeck TS, Gaut JP, Swamidass SJ. Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections. IEEE Trans Med Imaging. 2018;37:2718-2728. [PMID: 29994669 DOI: 10.1109/tmi.2018.2851150] [Cited by in Crossref: 49] [Cited by in F6Publishing: 22] [Article Influence: 12.3] [Reference Citation Analysis]
35 Ramachandra Kurup Sasikala A, Unnithan AR, Thomas RG, Batgerel T, Jeong YY, Park CH, Kim CS. Hexa-functional tumour-seeking nano voyagers and annihilators for synergistic cancer theranostic applications. Nanoscale 2018;10:19568-78. [DOI: 10.1039/c8nr06116e] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
36 Anderson M, Pitchforth E, Asaria M, Brayne C, Casadei B, Charlesworth A, Coulter A, Franklin BD, Donaldson C, Drummond M, Dunnell K, Foster M, Hussey R, Johnson P, Johnston-Webber C, Knapp M, Lavery G, Longley M, Clark JM, Majeed A, McKee M, Newton JN, O'Neill C, Raine R, Richards M, Sheikh A, Smith P, Street A, Taylor D, Watt RG, Whyte M, Woods M, McGuire A, Mossialos E. LSE-Lancet Commission on the future of the NHS: re-laying the foundations for an equitable and efficient health and care service after COVID-19. Lancet 2021;397:1915-78. [PMID: 33965070 DOI: 10.1016/S0140-6736(21)00232-4] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
37 Saini G, Mittal K, Rida P, Janssen EAM, Gogineni K, Aneja R. Panoptic View of Prognostic Models for Personalized Breast Cancer Management. Cancers (Basel) 2019;11:E1325. [PMID: 31500225 DOI: 10.3390/cancers11091325] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
38 Shamai G, Binenbaum Y, Slossberg R, Duek I, Gil Z, Kimmel R. Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer. JAMA Netw Open 2019;2:e197700. [PMID: 31348505 DOI: 10.1001/jamanetworkopen.2019.7700] [Cited by in Crossref: 37] [Cited by in F6Publishing: 25] [Article Influence: 12.3] [Reference Citation Analysis]
39 Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus? J Med Artif Intell 2019;2:12. [PMID: 31372599 DOI: 10.21037/jmai.2019.05.02] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
40 Chlis NK, Rausch L, Brocker T, Kranich J, Theis FJ. Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning. Nucleic Acids Res 2020;48:11335-46. [PMID: 33119742 DOI: 10.1093/nar/gkaa926] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Holzlechner M, Eugenin E, Prideaux B. Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer. Cancer Rep (Hoboken) 2019;2:e1229. [PMID: 32729258 DOI: 10.1002/cnr2.1229] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
42 Pallua JD, Brunner A, Zelger B, Schirmer M, Haybaeck J. The future of pathology is digital. Pathol Res Pract 2020;216:153040. [PMID: 32825928 DOI: 10.1016/j.prp.2020.153040] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
43 Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, Christiani DC, Mak RH, Aerts HJWL. Deep learning classification of lung cancer histology using CT images. Sci Rep 2021;11:5471. [PMID: 33727623 DOI: 10.1038/s41598-021-84630-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
44 Van Es SL. Digital pathology: semper ad meliora. Pathology 2019;51:1-10. [PMID: 30522785 DOI: 10.1016/j.pathol.2018.10.011] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 3.5] [Reference Citation Analysis]
45 Jang H, Song IH, Lee SH. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers. Applied Sciences 2021;11:808. [DOI: 10.3390/app11020808] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
46 Rączkowski Ł, Możejko M, Zambonelli J, Szczurek E. ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Sci Rep 2019;9:14347. [PMID: 31586139 DOI: 10.1038/s41598-019-50587-1] [Cited by in Crossref: 22] [Cited by in F6Publishing: 18] [Article Influence: 7.3] [Reference Citation Analysis]
47 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]
48 Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, Cossic B, Himmel LE, Rudmann DG, Bawa B, Muthuswamy A, Aina OH, Edmondson EF, Saravanan C, Brown DL, Sing T, Sebastian MM. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol Pathol 2020;48:277-94. [DOI: 10.1177/0192623319881401] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 5.3] [Reference Citation Analysis]
49 Balluet M, Sizaire F, El Habouz Y, Walter T, Pont J, Giroux B, Bouchareb O, Tramier M, Pecreaux J. Neural network fast-classifies biological images through features selecting to power automated microscopy. J Microsc 2021. [PMID: 34623634 DOI: 10.1111/jmi.13062] [Reference Citation Analysis]
50 Kernbach JM, Hakvoort K, Ort J, Clusmann H, Neuloh G, Delev D. The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI. Acta Neurochir Suppl 2022;134:257-61. [PMID: 34862549 DOI: 10.1007/978-3-030-85292-4_29] [Reference Citation Analysis]
51 Azuaje F, Kim SY, Perez Hernandez D, Dittmar G. Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning. J Clin Med 2019;8:E1535. [PMID: 31557788 DOI: 10.3390/jcm8101535] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
52 Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. Am J Pathol 2021:S0002-9440(21)00303-5. [PMID: 34252382 DOI: 10.1016/j.ajpath.2021.06.011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
53 Akbari Lakeh M, Tu A, Muddiman DC, Abdollahi H. Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis. Rapid Commun Mass Spectrom 2019;33:381-91. [PMID: 30468547 DOI: 10.1002/rcm.8362] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
54 Grenier K, Kao J, Diamandis P. Three-dimensional modeling of human neurodegeneration: brain organoids coming of age. Mol Psychiatry 2020;25:254-74. [PMID: 31444473 DOI: 10.1038/s41380-019-0500-7] [Cited by in Crossref: 34] [Cited by in F6Publishing: 33] [Article Influence: 11.3] [Reference Citation Analysis]
55 Marsh JN, Liu TC, Wilson PC, Swamidass SJ, Gaut JP. Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens. JAMA Netw Open 2021;4:e2030939. [PMID: 33471115 DOI: 10.1001/jamanetworkopen.2020.30939] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
56 Salvucci M, Zakaria Z, Carberry S, Tivnan A, Seifert V, Kögel D, Murphy BM, Prehn JHM. System-based approaches as prognostic tools for glioblastoma. BMC Cancer 2019;19:1092. [PMID: 31718568 DOI: 10.1186/s12885-019-6280-2] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
57 Izadyyazdanabadi M, Belykh E, Mooney MA, Eschbacher JM, Nakaji P, Yang Y, Preul MC. Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning. Front Oncol 2018;8:240. [PMID: 30035099 DOI: 10.3389/fonc.2018.00240] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 3.8] [Reference Citation Analysis]
58 La Perle KMD. Machine Learning and Veterinary Pathology: Be Not Afraid! Vet Pathol 2019;56:506-7. [PMID: 31185880 DOI: 10.1177/0300985819848504] [Reference Citation Analysis]
59 Xie Q, Faust K, Van Ommeren R, Sheikh A, Djuric U, Diamandis P. Deep learning for image analysis: Personalizing medicine closer to the point of care. Crit Rev Clin Lab Sci 2019;56:61-73. [PMID: 30628494 DOI: 10.1080/10408363.2018.1536111] [Cited by in Crossref: 22] [Cited by in F6Publishing: 14] [Article Influence: 7.3] [Reference Citation Analysis]
60 Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2018;15:20170703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Cited by in Crossref: 52] [Cited by in F6Publishing: 40] [Article Influence: 17.3] [Reference Citation Analysis]
61 Zhang L, Wu Y, Zheng B, Su L, Chen Y, Ma S, Hu Q, Zou X, Yao L, Yang Y, Chen L, Mao Y, Chen Y, Ji M. Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy. Theranostics 2019;9:2541-54. [PMID: 31131052 DOI: 10.7150/thno.32655] [Cited by in Crossref: 33] [Cited by in F6Publishing: 32] [Article Influence: 11.0] [Reference Citation Analysis]
62 Schau GF, Burlingame EA, Thibault G, Anekpuritanang T, Wang Y, Gray JW, Corless C, Chang YH. Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin. J Med Imaging (Bellingham) 2020;7:012706. [PMID: 34541020 DOI: 10.1117/1.JMI.7.1.012706] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
63 Doan M, Carpenter AE. Leveraging machine vision in cell-based diagnostics to do more with less. Nat Mater 2019;18:414-8. [DOI: 10.1038/s41563-019-0339-y] [Cited by in Crossref: 28] [Cited by in F6Publishing: 20] [Article Influence: 9.3] [Reference Citation Analysis]
64 Naqa IE, Kosorok MR, Jin J, Mierzwa M, Ten Haken RK. Prospects and challenges for clinical decision support in the era of big data. JCO Clin Cancer Inform 2018;2. [PMID: 30613823 DOI: 10.1200/CCI.18.00002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
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