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Cited by in F6Publishing
For: Sakamoto T, Furukawa T, Lami K, Pham HHN, Uegami W, Kuroda K, Kawai M, Sakanashi H, Cooper LAD, Bychkov A, Fukuoka J. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res 2020;9:2255-76. [PMID: 33209648 DOI: 10.21037/tlcr-20-591] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Ailia MJ, Thakur N, Abdul-ghafar J, Jung CK, Yim K, Chong Y. Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape. Cancers 2022;14:2400. [DOI: 10.3390/cancers14102400] [Reference Citation Analysis]
3 Yao Z, Jin T, Mao B, Lu B, Zhang Y, Li S, Chen W. Construction and Multicenter Diagnostic Verification of Intelligent Recognition System for Endoscopic Images From Early Gastric Cancer Based on YOLO-V3 Algorithm. Front Oncol 2022;12:815951. [DOI: 10.3389/fonc.2022.815951] [Reference Citation Analysis]
4 Eloy C, Bychkov A, Pantanowitz L, Fraggetta F, Bui MM, Fukuoka J, Zerbe N, Hassell L, Parwani A. DPA-ESDIP-JSDP Task Force for Worldwide Adoption of Digital Pathology. J Pathol Inform 2021;12:51. [PMID: 35070480 DOI: 10.4103/jpi.jpi_65_21] [Reference Citation Analysis]
5 Guo H, Diao L, Zhou X, Chen JN, Zhou Y, Fang Q, He Y, Dziadziuszko R, Zhou C, Hirsch FR. Artificial intelligence-based analysis for immunohistochemistry staining of immune checkpoints to predict resected non-small cell lung cancer survival and relapse. Transl Lung Cancer Res 2021;10:2452-74. [PMID: 34295654 DOI: 10.21037/tlcr-21-96] [Reference Citation Analysis]
6 Chen S, Gao Y. Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms. Journal of Healthcare Engineering 2022;2022:1-12. [DOI: 10.1155/2022/3972298] [Reference Citation Analysis]
7 Giovagnoli MR, Ciucciarelli S, Castrichella L, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders. Healthcare (Basel) 2021;9:1347. [PMID: 34683027 DOI: 10.3390/healthcare9101347] [Reference Citation Analysis]
8 Uegami W, Bychkov A, Ozasa M, Uehara K, Kataoka K, Johkoh T, Kondoh Y, Sakanashi H, Fukuoka J. MIXTURE of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease. Mod Pathol 2022. [PMID: 35197560 DOI: 10.1038/s41379-022-01025-7] [Reference Citation Analysis]
9 Feng Y, Chen K, Pan L, Jiang W, Pang P, Mao G, Zhang B, Chen S. RPB5-mediating protein promotes the progression of non-small cell lung cancer by regulating the proliferation and invasion. J Thorac Dis 2021;13:299-311. [PMID: 33569210 DOI: 10.21037/jtd-20-3461] [Reference Citation Analysis]
10 Zaizen Y, Kanahori Y, Ishijima S, Kitamura Y, Yoon H, Ozasa M, Mukae H, Bychkov A, Hoshino T, Fukuoka J. Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests. Diagnostics 2022;12:709. [DOI: 10.3390/diagnostics12030709] [Reference Citation Analysis]
11 Lin YJ, Chao TK, Khalil MA, Lee YC, Hong DZ, Wu JJ, Wang CW. Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis. Cancers (Basel) 2021;13:3891. [PMID: 34359792 DOI: 10.3390/cancers13153891] [Reference Citation Analysis]
12 Darbari A, Kumar K, Darbari S, Patil PL. Requirement of artificial intelligence technology awareness for thoracic surgeons. Cardiothorac Surg 2021;29. [DOI: 10.1186/s43057-021-00053-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]