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Cited by in F6Publishing
For: Ramot Y, Zandani G, Madar Z, Deshmukh S, Nyska A. Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice. Toxicol Pathol 2020;48:702-7. [PMID: 32508268 DOI: 10.1177/0192623320926478] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 Feng G, Zheng KI, Li YY, Rios RS, Zhu PW, Pan XY, Li G, Ma HL, Tang LJ, Byrne CD, Targher G, He N, Mi M, Chen YP, Zheng MH. Machine learning algorithm outperforms fibrosis markers in predicting significant fibrosis in biopsy-confirmed NAFLD. J Hepatobiliary Pancreat Sci 2021;28:593-603. [PMID: 33908180 DOI: 10.1002/jhbp.972] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 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]
5 Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2021. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Reference Citation Analysis]
6 Ramot Y, Deshpande A, Morello V, Michieli P, Shlomov T, Nyska A. Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm. Toxicol Pathol 2021;49:1126-33. [PMID: 33769147 DOI: 10.1177/01926233211003866] [Reference Citation Analysis]
7 Hwang JH, Kim HJ, Park H, Lee BS, Son HY, Kim YB, Jun SY, Park JH, Lee J, Cho JW. Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats. Toxicol Pathol 2021;:1926233211057128. [PMID: 34866512 DOI: 10.1177/01926233211057128] [Reference Citation Analysis]
8 De Vera Mudry MC, Martin J, Schumacher V, Venugopal R. Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy. Toxicol Pathol 2021;49:851-61. [PMID: 33371793 DOI: 10.1177/0192623320980674] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]