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For: Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166. [PMID: 32277744 DOI: 10.1002/cac2.12012] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 Wang X, Li BB. Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet 2021;12:624820. [PMID: 33643386 DOI: 10.3389/fgene.2021.624820] [Reference Citation Analysis]
4 Tokarz DA, Steinbach TJ, Lokhande A, Srivastava G, Ugalmugle R, Co CA, Shockley KR, Singletary E, Cesta MF, Thomas HC, Chen VS, Hobbie K, Crabbs TA. Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy. Toxicol Pathol 2021;49:888-96. [PMID: 33287662 DOI: 10.1177/0192623320972614] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
5 Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, Hekler A, Alwers E, von Kalle C, Fröhling S, Utikal JS, Brenner H, Hoffmeister M, Brinker TJ. Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. Eur J Cancer 2021;155:200-15. [PMID: 34391053 DOI: 10.1016/j.ejca.2021.07.012] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
6 Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166. [PMID: 32277744 DOI: 10.1002/cac2.12012] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [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 He Y, Zhao H, Wong STC. Deep learning powers cancer diagnosis in digital pathology. Comput Med Imaging Graph 2021;88:101820. [PMID: 33453648 DOI: 10.1016/j.compmedimag.2020.101820] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Ullah M, Akbar A, Yannarelli G. Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine. Artif Intell Cancer 2020; 1(2): 39-44 [DOI: 10.35713/aic.v1.i2.39] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
10 Jahn SW, Plass M, Moinfar F. Digital Pathology: Advantages, Limitations and Emerging Perspectives. J Clin Med 2020;9:E3697. [PMID: 33217963 DOI: 10.3390/jcm9113697] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
11 Steinbuss G, Kriegsmann M, Zgorzelski C, Brobeil A, Goeppert B, Dietrich S, Mechtersheimer G, Kriegsmann K. Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images. Cancers (Basel) 2021;13:2419. [PMID: 34067726 DOI: 10.3390/cancers13102419] [Reference Citation Analysis]
12 Al-Qudah R, Suen CY. Improving blood cells classification in peripheral blood smears using enhanced incremental training. Comput Biol Med 2021;131:104265. [PMID: 33621895 DOI: 10.1016/j.compbiomed.2021.104265] [Reference Citation Analysis]
13 Fitzgerald J, Higgins D, Mazo Vargas C, Watson W, Mooney C, Rahman A, Aspell N, Connolly A, Aura Gonzalez C, Gallagher W. Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer. J Clin Pathol 2021;74:429-34. [PMID: 34117103 DOI: 10.1136/jclinpath-2020-207351] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Tanabe S. Cancer recognition of artificial intelligence. Artif Intell Cancer 2021; 2(1): 1-6 [DOI: 10.35713/aic.v2.i1.1] [Reference Citation Analysis]
16 van der Kamp A, Waterlander TJ, de Bel T, van der Laak J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, de Krijger RR. Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future? Pediatr Dev Pathol 2022;:10935266211059809. [PMID: 35238696 DOI: 10.1177/10935266211059809] [Reference Citation Analysis]
17 Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol 2021. [PMID: 34561876 DOI: 10.1002/path.5800] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Kriegsmann M, Kriegsmann K, Steinbuss G, Zgorzelski C, Kraft A, Gaida MM. Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma. Int J Mol Sci 2021;22:5385. [PMID: 34065423 DOI: 10.3390/ijms22105385] [Reference Citation Analysis]
19 Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021;41:1100-15. [PMID: 34613667 DOI: 10.1002/cac2.12215] [Reference Citation Analysis]
20 Esce AR, Redemann JP, Sanchez AC, Olson GT, Hanson JA, Agarwal S, Boyd NH, Martin DR. Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence. Am J Surg 2021:S0002-9610(21)00281-6. [PMID: 34030870 DOI: 10.1016/j.amjsurg.2021.05.002] [Reference Citation Analysis]