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For: Roohi A, Faust K, Djuric U, Diamandis P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surg Pathol Clin 2020;13:349-58. [PMID: 32389272 DOI: 10.1016/j.path.2020.01.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Jin W, Luo Q. When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation. Computers in Biology and Medicine 2022;145:105499. [DOI: 10.1016/j.compbiomed.2022.105499] [Reference Citation Analysis]
2 Rabbani N, Kim GY, Suarez CJ, Chen JH. Applications of Machine Learning in Routine Laboratory Medicine: Current State and Future Directions. Clinical Biochemistry 2022. [DOI: 10.1016/j.clinbiochem.2022.02.011] [Reference Citation Analysis]
3 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]
4 Lee K, Lockhart JH, Xie M, Chaudhary R, Slebos RJC, Flores ER, Chung CH, Tan AC. Deep Learning of Histopathology Images at the Single Cell Level. Front Artif Intell 2021;4:754641. [PMID: 34568816 DOI: 10.3389/frai.2021.754641] [Reference Citation Analysis]
5 Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020;2:569178. [DOI: 10.3389/fdgth.2020.569178] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Valous NA, Moraleda RR, Jäger D, Zörnig I, Halama N. Interrogating the microenvironmental landscape of tumors with computational image analysis approaches. Semin Immunol 2020;48:101411. [PMID: 33168423 DOI: 10.1016/j.smim.2020.101411] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 Dagli MM, Rajesh A, Asaad M, Butler CE. The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review. Am Surg 2021;:31348211065101. [PMID: 34958252 DOI: 10.1177/00031348211065101] [Reference Citation Analysis]
8 de Oliveira ECL, da Costa KS, Taube PS, Lima AH, Junior CDSDS. Biological Membrane-Penetrating Peptides: Computational Prediction and Applications. Front Cell Infect Microbiol 2022;12:838259. [DOI: 10.3389/fcimb.2022.838259] [Reference Citation Analysis]
9 Church DL, Naugler C. Using a systematic approach to strategic innovation in laboratory medicine to bring about change. Critical Reviews in Clinical Laboratory Sciences. [DOI: 10.1080/10408363.2021.1997899] [Reference Citation Analysis]
10 McAlpine ED, Michelow P, Celik T. The Utility of Unsupervised Machine Learning in Anatomic Pathology. Am J Clin Pathol 2021:aqab085. [PMID: 34302331 DOI: 10.1093/ajcp/aqab085] [Reference Citation Analysis]
11 Zin F, Cotter JA, Haberler C, Dottermusch M, Neumann J, Schüller U, Schweizer L, Thomas C, Nemes K, Johann PD, Kool M, Frühwald MC, Paulus W, Judkins A, Hasselblatt M. Histopathological patterns in atypical teratoid/rhabdoid tumors are related to molecular subgroup. Brain Pathol 2021;31:e12967. [PMID: 33938067 DOI: 10.1111/bpa.12967] [Reference Citation Analysis]
12 Hariharan R. Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi. Urban Clim 2021;36:100780. [PMID: 33520641 DOI: 10.1016/j.uclim.2021.100780] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]