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For: 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;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 169] [Article Influence: 63.7] [Reference Citation Analysis]
Number Citing Articles
1 Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2021. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Daisy PS, Anitha TS. Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy? Med Oncol 2021;38:53. [PMID: 33811540 DOI: 10.1007/s12032-021-01500-2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Ahmedt-aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology. Computerized Medical Imaging and Graphics 2022;95:102027. [DOI: 10.1016/j.compmedimag.2021.102027] [Reference Citation Analysis]
4 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
5 Bianconi F, Kather JN, Reyes-Aldasoro CC. Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin. Cancers (Basel) 2020;12:E3337. [PMID: 33187299 DOI: 10.3390/cancers12113337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
6 Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 2021. [PMID: 34611303 DOI: 10.1038/s41379-021-00919-2] [Reference Citation Analysis]
7 Durkee MS, Abraham R, Clark MR, Giger ML. Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images. Am J Pathol 2021:S0002-9440(21)00261-3. [PMID: 34129842 DOI: 10.1016/j.ajpath.2021.05.022] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
8 Zhang J, Hua Z, Yan K, Tian K, Yao J, Liu E, Liu M, Han X. Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images. Med Image Anal 2021;73:102183. [PMID: 34340108 DOI: 10.1016/j.media.2021.102183] [Reference Citation Analysis]
9 Wang R, Wang S, Duan N, Wang Q. From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives. Front Med (Lausanne) 2020;7:145. [PMID: 32671076 DOI: 10.3389/fmed.2020.00145] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
10 Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021:S1044-579X(21)00034-1. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-8. [PMID: 33721700 DOI: 10.1016/j.ejmp.2021.02.024] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Giovagnoli MR, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards. Healthcare (Basel) 2021;9:858. [PMID: 34356236 DOI: 10.3390/healthcare9070858] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Baptiste M, Moinuddeen SS, Soliz CL, Ehsan H, Kaneko G. Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning. Genes (Basel) 2021;12:722. [PMID: 34065872 DOI: 10.3390/genes12050722] [Reference Citation Analysis]
14 Grimm LJ, Rahbar H, Abdelmalak M, Hall AH, Ryser MD. Ductal Carcinoma in Situ: State-of-the-Art Review. Radiology. [DOI: 10.1148/radiol.211839] [Reference Citation Analysis]
15 Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020;3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Cited by in Crossref: 32] [Cited by in F6Publishing: 13] [Article Influence: 16.0] [Reference Citation Analysis]
16 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Gowda V, Kwaramba T, Hanemann C, Garcia JA, Barata PC. Artificial Intelligence in Cancer Care: Legal and Regulatory Dimensions. Oncologist 2021;26:807-10. [PMID: 34137481 DOI: 10.1002/onco.13862] [Reference Citation Analysis]
18 Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal 2021;67:101813. [PMID: 33049577 DOI: 10.1016/j.media.2020.101813] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 13.0] [Reference Citation Analysis]
19 Keikhosravi A, Li B, Liu Y, Conklin MW, Loeffler AG, Eliceiri KW. Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis. Commun Biol 2020;3:414. [PMID: 32737412 DOI: 10.1038/s42003-020-01151-5] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
20 Pischon H, Mason D, Lawrenz B, Blanck O, Frisk AL, Schorsch F, Bertani V. Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats. Toxicol Pathol 2021;49:928-37. [PMID: 33397216 DOI: 10.1177/0192623320983244] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
21 Lin HM, Xue XF, Wang XG, Dang SC, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. Artif Intell Gastroenterol 2020; 1(1): 19-29 [DOI: 10.35712/aig.v1.i1.19] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
22 Walens A, Olsson LT, Gao X, Hamilton AM, Kirk EL, Cohen SM, Midkiff BR, Xia Y, Sherman ME, Nikolaishvili-Feinberg N, Serody JS, Hoadley KA, Troester MA, Calhoun BC. Protein-based immune profiles of basal-like vs. luminal breast cancers. Lab Invest 2021;101:785-93. [PMID: 33623115 DOI: 10.1038/s41374-020-00506-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Hu D, Wang C, Zheng S, Cui X. Investigating the genealogy of the literature on digital pathology: a two-dimensional bibliometric approach. Scientometrics. [DOI: 10.1007/s11192-021-04224-2] [Reference Citation Analysis]
24 Wu X, Yao Y, Dai Y, Diao P, Zhang Y, Zhang P, Li S, Jiang H, Cheng J. Identification of diagnostic and prognostic signatures derived from preoperative blood parameters for oral squamous cell carcinoma. Ann Transl Med 2021;9:1220. [PMID: 34532357 DOI: 10.21037/atm-21-631] [Reference Citation Analysis]
25 Mao Y, Wang X, Huang P, Tian R. Spatial proteomics for understanding the tissue microenvironment. Analyst 2021;146:3777-98. [PMID: 34042124 DOI: 10.1039/d1an00472g] [Reference Citation Analysis]
26 Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, Wistuba II, Rimm DL, Tsao MS, Hirsch FR. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol 2021;18:345-62. [PMID: 33580222 DOI: 10.1038/s41571-021-00473-5] [Cited by in Crossref: 27] [Cited by in F6Publishing: 27] [Article Influence: 27.0] [Reference Citation Analysis]
27 Taylor-Weiner A, Pokkalla H, Han L, Jia C, Huss R, Chung C, Elliott H, Glass B, Pethia K, Carrasco-Zevallos O, Shukla C, Khettry U, Najarian R, Taliano R, Subramanian GM, Myers RP, Wapinski I, Khosla A, Resnick M, Montalto MC, Anstee QM, Wong VW, Trauner M, Lawitz EJ, Harrison SA, Okanoue T, Romero-Gomez M, Goodman Z, Loomba R, Beck AH, Younossi ZM. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH. Hepatology 2021;74:133-47. [PMID: 33570776 DOI: 10.1002/hep.31750] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
28 Harmon SA, Sanford TH, Brown GT, Yang C, Mehralivand S, Jacob JM, Valera VA, Shih JH, Agarwal PK, Choyke PL, Turkbey B. Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer. JCO Clin Cancer Inform 2020;4:367-82. [PMID: 32330067 DOI: 10.1200/CCI.19.00155] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
29 Koyuncu CF, Lu C, Bera K, Zhang Z, Xu J, Toro P, Corredor G, Chute D, Fu P, Thorstad WL, Faraji F, Bishop JA, Mehrad M, Castro PD, Sikora AG, Thompson LD, Chernock RD, Lang Kuhs KA, Luo J, Sandulache V, Adelstein DJ, Koyfman S, Lewis JS Jr, Madabhushi A. Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. J Clin Invest 2021;131:145488. [PMID: 33651718 DOI: 10.1172/JCI145488] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
30 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
31 Prince EA, Sanzari JK, Pandya D, Huron D, Edwards R. Analytical Concordance of PD-L1 Assays Utilizing Antibodies From FDA-Approved Diagnostics in Advanced Cancers: A Systematic Literature Review. JCO Precis Oncol 2021;5:953-73. [PMID: 34136742 DOI: 10.1200/PO.20.00412] [Reference Citation Analysis]
32 Gallins P, Saghapour E, Zhou YH. Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes. Front Genet 2020;11:555886. [PMID: 33193632 DOI: 10.3389/fgene.2020.555886] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
33 Jayachandran S, Ghosh A. Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology. In: Schilling F, Stadelmann T, editors. Artificial Neural Networks in Pattern Recognition. Cham: Springer International Publishing; 2020. pp. 173-86. [DOI: 10.1007/978-3-030-58309-5_14] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
34 Gambardella V, Bruixola G, Alfaro C, Cervantes A. In the literature: October 2020. ESMO Open 2020;5:e001048. [PMID: 33037034 DOI: 10.1136/esmoopen-2020-001048] [Reference Citation Analysis]
35 Kosaraju SC, Hao J, Koh HM, Kang M. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis. Methods. 2020;179:3-13. [PMID: 32442672 DOI: 10.1016/j.ymeth.2020.05.012] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
36 Borhani S, Borhani R, Kajdacsy-balla A. Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction. Critical Reviews in Oncology/Hematology 2022. [DOI: 10.1016/j.critrevonc.2022.103601] [Reference Citation Analysis]
37 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]
38 Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021;5:203-18. [PMID: 33589781 DOI: 10.1038/s41551-020-00681-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
39 Mulliqi N, Kartasalo K, Olsson H, Ji X, Egevad L, Eklund M, Ruusuvuori P. OpenPhi: An interface to access Philips iSyntax whole slide images for computational pathology. Bioinformatics 2021:btab578. [PMID: 34358287 DOI: 10.1093/bioinformatics/btab578] [Reference Citation Analysis]
40 Rafique R, Islam SMR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021;19:4003-17. [PMID: 34377366 DOI: 10.1016/j.csbj.2021.07.003] [Reference Citation Analysis]
41 Abraham J, Heimberger AB, Marshall J, Heath E, Drabick J, Helmstetter A, Xiu J, Magee D, Stafford P, Nabhan C, Antani S, Johnston C, Oberley M, Korn WM, Spetzler D. Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type. Transl Oncol 2021;14:101016. [PMID: 33465745 DOI: 10.1016/j.tranon.2021.101016] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
42 Liu K, Xia W, Qiang M, Chen X, Liu J, Guo X, Lv X. Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy. Cancer Med 2020;9:1298-306. [PMID: 31860791 DOI: 10.1002/cam4.2802] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
43 Aboujaoude E, Gega L, Parish MB, Hilty DM. Editorial: Digital Interventions in Mental Health: Current Status and Future Directions. Front Psychiatry 2020;11:111. [PMID: 32174858 DOI: 10.3389/fpsyt.2020.00111] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
44 Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021. [PMID: 34874486 DOI: 10.1007/s11596-021-2474-3] [Reference Citation Analysis]
45 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
46 Kristiansen G, Schmid M. Application of computer-generated images to train pattern recognition used in semiquantitative immunohistochemistry scoring. APMIS 2021. [PMID: 34748225 DOI: 10.1111/apm.13188] [Reference Citation Analysis]
47 Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021;101:412-22. [PMID: 33454724 DOI: 10.1038/s41374-020-00514-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
48 Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14. [PMID: 32550270 DOI: 10.1038/s41698-020-0120-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 10.0] [Reference Citation Analysis]
49 Peyster EG, Arabyarmohammadi S, Janowczyk A, Azarianpour-Esfahani S, Sekulic M, Cassol C, Blower L, Parwani A, Lal P, Feldman MD, Margulies KB, Madabhushi A. An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. Eur Heart J 2021;42:2356-69. [PMID: 33982079 DOI: 10.1093/eurheartj/ehab241] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
50 Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Eklund L, Jambor I, Merisaari H, Ettala O, Taimen P, Aronen HJ, Boström PJ, Tewari A, Magi-Galluzzi C, Klein E, Purysko A, Nc Shih N, Feldman M, Gupta S, Lal P, Madabhushi A. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precis Oncol 2021;5:35. [PMID: 33941830 DOI: 10.1038/s41698-021-00174-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
51 Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Hodgin JB, Zee J, Hewitt SM, O'Toole J, Toro P, Sedor JR, Barisoni L, Madabhushi A; Nephrotic Syndrome Study Network (NEPTUNE). Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int 2021;99:86-101. [PMID: 32835732 DOI: 10.1016/j.kint.2020.07.044] [Cited by in Crossref: 14] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
52 Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021;11:1078. [PMID: 34204822 DOI: 10.3390/diagnostics11061078] [Reference Citation Analysis]
53 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]
54 Marti-aguado D, Fernández-patón M, Alfaro-cervello C, Mestre-alagarda C, Bauza M, Gallen-peris A, Merino V, Benlloch S, Pérez-rojas J, Ferrández A, Puglia V, Gimeno-torres M, Aguilera V, Monton C, Escudero-garcía D, Alberich-bayarri Á, Serra MA, Marti-bonmati L. Digital Pathology Enables Automated and Quantitative Assessment of Inflammatory Activity in Patients with Chronic Liver Disease. Biomolecules 2021;11:1808. [DOI: 10.3390/biom11121808] [Reference Citation Analysis]
55 De Las Casas LE, Hicks DG. Pathologists at the Leading Edge of Optimizing the Tumor Tissue Journey for Diagnostic Accuracy and Molecular Testing. Am J Clin Pathol 2021;155:781-92. [PMID: 33582767 DOI: 10.1093/ajcp/aqaa212] [Reference Citation Analysis]
56 Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today 2021;26:511-24. [PMID: 33346134 DOI: 10.1016/j.drudis.2020.12.009] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 5.5] [Reference Citation Analysis]
57 Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Shih NNC, Feldman M, Gupta S, McKenney J, Lal P, Madabhushi A. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. Eur Urol Focus 2021;7:722-32. [PMID: 33941504 DOI: 10.1016/j.euf.2021.04.016] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 Bender A, Cortes-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov Today 2021;26:1040-52. [PMID: 33508423 DOI: 10.1016/j.drudis.2020.11.037] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
59 Zhang F, Zhong LZ, Zhao X, Dong D, Yao JJ, Wang SY, Liu Y, Zhu D, Wang Y, Wang GJ, Wang YM, Li D, Wei J, Tian J, Shan H. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study. Ther Adv Med Oncol 2020;12:1758835920971416. [PMID: 33403013 DOI: 10.1177/1758835920971416] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease. Aging Dis 2020;11:1567-84. [PMID: 33269107 DOI: 10.14336/AD.2020.0312] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
61 Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med 2020;14:369-81. [PMID: 32621201 DOI: 10.1007/s11684-020-0784-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
62 Lu C, Koyuncu C, Corredor G, Prasanna P, Leo P, Wang X, Janowczyk A, Bera K, Lewis J Jr, Velcheti V, Madabhushi A. Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Med Image Anal 2021;68:101903. [PMID: 33352373 DOI: 10.1016/j.media.2020.101903] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
63 Lagree A, Mohebpour M, Meti N, Saednia K, Lu FI, Slodkowska E, Gandhi S, Rakovitch E, Shenfield A, Sadeghi-Naini A, Tran WT. A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Sci Rep 2021;11:8025. [PMID: 33850222 DOI: 10.1038/s41598-021-87496-1] [Reference Citation Analysis]
64 Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, Herbig M. AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. Adv Sci (Weinh) 2021;8:e2003743. [PMID: 34105281 DOI: 10.1002/advs.202003743] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
65 Thomas SM, Lefevre JG, Baxter G, Hamilton NA. Characterization of tissue types in basal cell carcinoma images via generative modeling and concept vectors. Comput Med Imaging Graph 2021;94:101998. [PMID: 34656812 DOI: 10.1016/j.compmedimag.2021.101998] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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