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Cited by in F6Publishing
For: Nam S, Chong Y, Jung CK, Kwak TY, Lee JY, Park J, Rho MJ, Go H. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med 2020;54:125-34. [PMID: 32045965 DOI: 10.4132/jptm.2019.12.31] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 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]
4 Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu FI, Dodington D, Gandhi S, Slodkowska EA, Shenfield A, Jerzak KJ, Sadeghi-Naini A, Tran WT. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Curr Oncol 2021;28:4298-316. [PMID: 34898544 DOI: 10.3390/curroncol28060366] [Reference Citation Analysis]
5 Chong Y, Kim DC, Jung CK, Kim DC, Song SY, Joo HJ, Yi SY; Medical Informatics Study Group of the Korean Society of Pathologists. Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists. J Pathol Transl Med 2020;54:437-52. [PMID: 33027850 DOI: 10.4132/jptm.2020.08.27] [Reference Citation Analysis]
6 Dias EP, Oliveira NSC, Serra-Campos AO, da Silva AKF, da Silva LE, Cunha KS. A novel evaluation method for Ki-67 immunostaining in paraffin-embedded tissues. Virchows Arch 2021;479:121-31. [PMID: 33464376 DOI: 10.1007/s00428-020-03010-4] [Reference Citation Analysis]
7 Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020;18:2312-25. [PMID: 32994890 DOI: 10.1016/j.csbj.2020.08.003] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
8 Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers (Basel). 2020;12. [PMID: 32668721 DOI: 10.3390/cancers12071884] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
9 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]
10 Schuettfort VM, Pradere B, Rink M, Comperat E, Shariat SF. Pathomics in urology. Curr Opin Urol 2020;30:823-31. [PMID: 32881725 DOI: 10.1097/MOU.0000000000000813] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
11 Xue P, Ng MTA, Qiao Y. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence. BMC Med. 2020;18:169. [PMID: 32493320 DOI: 10.1186/s12916-020-01613-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
12 Bertani V, Blanck O, Guignard D, Schorsch F, Pischon H. Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models. Toxicol Pathol 2021;:1926233211052010. [PMID: 34670459 DOI: 10.1177/01926233211052010] [Reference Citation Analysis]
13 Roszkowiak L, Korzynska A, Pijanowska D, Bosch R, Lejeune M, Lopez C. Clustered nuclei splitting based on recurrent distance transform in digital pathology images. J Image Video Proc 2020;2020. [DOI: 10.1186/s13640-020-00514-6] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
14 Lee M, Herrington CS, Ravindra M, Sepp K, Davies A, Hulme AN, Brunton VG. Recent advances in the use of stimulated Raman scattering in histopathology. Analyst 2021;146:789-802. [PMID: 33393954 DOI: 10.1039/d0an01972k] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 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]
16 Chong Y, Thakur N, Lee JY, Hwang G, Choi M, Kim Y, Yu H, Cho MY. Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation. Diagn Pathol 2021;16:19. [PMID: 33706755 DOI: 10.1186/s13000-021-01081-8] [Reference Citation Analysis]
17 Chong Y, Lee JY, Kim Y, Choi J, Yu H, Park G, Cho MY, Thakur N. A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database. J Pathol Transl Med 2020;54:462-70. [PMID: 32854491 DOI: 10.4132/jptm.2020.07.11] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Aswathy MA, Jagannath M. An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features. Med Biol Eng Comput 2021;59:1773-83. [PMID: 34302269 DOI: 10.1007/s11517-021-02403-0] [Reference Citation Analysis]