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For: Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform 2018;9:38. [PMID: 30607305 DOI: 10.4103/jpi.jpi_53_18] [Cited by in Crossref: 113] [Cited by in F6Publishing: 87] [Article Influence: 28.3] [Reference Citation Analysis]
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8 Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol 2021;74:409-14. [PMID: 32763920 DOI: 10.1136/jclinpath-2020-206908] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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10 Arlova A, Jin C, Wong-Rolle A, Chen ES, Lisle C, Brown GT, Lay N, Choyke PL, Turkbey B, Harmon S, Zhao C. Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma. J Pathol Inform 2022;13:100007. [PMID: 35242446 DOI: 10.1016/j.jpi.2022.100007] [Reference Citation Analysis]
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12 Browning L, Fryer E, Roskell D, White K, Colling R, Rittscher J, Verrill C. Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital. J Clin Pathol 2021;74:129-32. [PMID: 32616541 DOI: 10.1136/jclinpath-2020-206786] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
13 Scodellaro R, Bouzin M, Mingozzi F, D'Alfonso L, Granucci F, Collini M, Chirico G, Sironi L. Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging. Front Oncol 2019;9:527. [PMID: 31275857 DOI: 10.3389/fonc.2019.00527] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
14 Salviato T, Bonetti LR, Mangogna A, Leoncini G, Cadei M, Caprioli F, Armuzzi A, Daperno M, Villanacci V. Microscopic imaging of Inflammatory Bowel Disease (IBD) and Non-IBD Colitis on digital slides: The Italian Group-IBD Pathologists experience. Pathol Res Pract 2020;216:153189. [PMID: 32906010 DOI: 10.1016/j.prp.2020.153189] [Reference Citation Analysis]
15 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]
16 Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019;249:286-94. [PMID: 31355445 DOI: 10.1002/path.5331] [Cited by in Crossref: 68] [Cited by in F6Publishing: 59] [Article Influence: 22.7] [Reference Citation Analysis]
17 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
18 Wu J, Liu C, Liu X, Sun W, Li L, Gao N, Zhang Y, Yang X, Zhang J, Wang H, Liu X, Huang X, Zhang Y, Cheng R, Chi K, Mao L, Zhou L, Lin D, Ling S. Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Mod Pathol 2021. [PMID: 34518630 DOI: 10.1038/s41379-021-00904-9] [Reference Citation Analysis]
19 Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 169] [Article Influence: 63.7] [Reference Citation Analysis]
20 Buja LM. The Texas Society of Pathologists: molded by the legacy of pathology and focused on excellence in medicine for 100 years and beyond. Proc (Bayl Univ Med Cent) 2020;34:199-214. [PMID: 33456200 DOI: 10.1080/08998280.2020.1812366] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, Mao Q, Yu H, Cai X. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020;4:14. [PMID: 32550270 DOI: 10.1038/s41698-020-0120-3] [Cited by in Crossref: 20] [Cited by in F6Publishing: 24] [Article Influence: 10.0] [Reference Citation Analysis]
22 Thamik H, Wu J. The Impact of Artificial Intelligence on Sustainable Development in Electronic Markets. Sustainability 2022;14:3568. [DOI: 10.3390/su14063568] [Reference Citation Analysis]
23 Petrick N, Akbar S, Cha KH, Nofech-Mozes S, Sahiner B, Gavrielides MA, Kalpathy-Cramer J, Drukker K, Martel AL; BreastPathQ Challenge Group. SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. J Med Imaging (Bellingham) 2021;8:034501. [PMID: 33987451 DOI: 10.1117/1.JMI.8.3.034501] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Abdeltawab H, Khalifa F, Ghazal M, Cheng L, Gondim D, El-Baz A. A pyramidal deep learning pipeline for kidney whole-slide histology images classification. Sci Rep 2021;11:20189. [PMID: 34642404 DOI: 10.1038/s41598-021-99735-6] [Reference Citation Analysis]
25 Perincheri S. Tumor Microenvironment of Lymphomas and Plasma Cell Neoplasms: Broad Overview and Impact on Evaluation for Immune Based Therapies. Front Oncol 2021;11:719140. [DOI: 10.3389/fonc.2021.719140] [Reference Citation Analysis]
26 Carse J, Mckenna S. Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs. In: Reyes-aldasoro CC, Janowczyk A, Veta M, Bankhead P, Sirinukunwattana K, editors. Digital Pathology. Cham: Springer International Publishing; 2019. pp. 20-7. [DOI: 10.1007/978-3-030-23937-4_3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
27 Muhsen IN, Shyr D, Sung AD, Hashmi SK. Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases. Clin Hematol Int 2021;3:13-20. [PMID: 34595462 DOI: 10.2991/chi.k.201130.001] [Reference Citation Analysis]
28 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
29 Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B 2021;11:3393-405. [PMID: 34900525 DOI: 10.1016/j.apsb.2021.02.007] [Reference Citation Analysis]
30 Badea L, Stănescu E. Identifying transcriptomic correlates of histology using deep learning. PLoS One 2020;15:e0242858. [PMID: 33237966 DOI: 10.1371/journal.pone.0242858] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 Chen P, Shi X, Liang Y, Li Y, Yang L, Gader PD. Interactive thyroid whole slide image diagnostic system using deep representation. Comput Methods Programs Biomed 2020;195:105630. [PMID: 32634647 DOI: 10.1016/j.cmpb.2020.105630] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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33 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]
34 Hodgin JB, Mariani LH. Automated Quantification of Chronic Changes in the Kidney Biopsy: Another Step in the Right Direction. J Am Soc Nephrol 2021:ASN. [PMID: 33685977 DOI: 10.1681/ASN.2021020240] [Reference Citation Analysis]
35 Pantanowitz L, Hartman D, Qi Y, Cho EY, Suh B, Paeng K, Dhir R, Michelow P, Hazelhurst S, Song SY, Cho SY. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses. Diagn Pathol 2020;15:80. [PMID: 32622359 DOI: 10.1186/s13000-020-00995-z] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
36 Diao S, Hou J, Yu H, Zhao X, Sun Y, Lambo RL, Xie Y, Liu L, Qin W, Luo W. Computer-Aided Pathological Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning. Am J Pathol. 2020;. [PMID: 32360568 DOI: 10.1016/j.ajpath.2020.04.008] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
37 Chen PS, Li YP, Ni HF. Morphology and Evaluation of Renal Fibrosis. Adv Exp Med Biol 2019;1165:17-36. [PMID: 31399959 DOI: 10.1007/978-981-13-8871-2_2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 1.7] [Reference Citation Analysis]
38 Hoenerhoff MJ, Meyerholz DK, Brayton C, Beck AP. Challenges and Opportunities for the Veterinary Pathologist in Biomedical Research. Vet Pathol 2021;58:258-65. [PMID: 33327888 DOI: 10.1177/0300985820974005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
39 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]
40 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]
41 van Hartskamp M, Consoli S, Verhaegh W, Petkovic M, van de Stolpe A. Artificial Intelligence in Clinical Health Care Applications: Viewpoint. Interact J Med Res 2019;8:e12100. [PMID: 30950806 DOI: 10.2196/12100] [Cited by in Crossref: 18] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
42 Thomas SM, Lefevre JG, Baxter G, Hamilton NA. Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Medical Image Analysis 2021;68:101915. [DOI: 10.1016/j.media.2020.101915] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
43 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]
44 Tien TZ, Lee JNLW, Lim JCT, Chen XY, Thike AA, Tan PH, Yeong JPS. Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence. Histopathology 2021;79:139-59. [PMID: 33400265 DOI: 10.1111/his.14328] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
45 Lujan G, Quigley JC, Hartman D, Parwani A, Roehmholdt B, Meter BV, Ardon O, Hanna MG, Kelly D, Sowards C, Montalto M, Bui M, Zarella MD, LaRosa V, Slootweg G, Retamero JA, Lloyd MC, Madory J, Bowman D. Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association. J Pathol Inform 2021;12:17. [PMID: 34221633 DOI: 10.4103/jpi.jpi_67_20] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
46 Khened M, Kori A, Rajkumar H, Krishnamurthi G, Srinivasan B. A generalized deep learning framework for whole-slide image segmentation and analysis. Sci Rep 2021;11:11579. [PMID: 34078928 DOI: 10.1038/s41598-021-90444-8] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Zhao S, Li F, Guo X, Guo T, Mizutani KI, Yamada S, Gu C, Uramoto H. New additional scoring formula on the Pathological Features in Stage I Lung Adenocarcinoma Patients: Impact on Survival. Int J Med Sci 2020;17:1871-8. [PMID: 32788866 DOI: 10.7150/ijms.45002] [Reference Citation Analysis]
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51 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]
52 Janardhan KS, Kohnken R, Turner OC, Gurumurthy CB, Kovi RC. Looking Forward: Cutting-Edge Technologies and Skills for Pathologists in the Future. Toxicol Pathol 2019;47:1082-7. [DOI: 10.1177/0192623319873855] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
53 Landau MS, Pantanowitz L. Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. Journal of the American Society of Cytopathology 2019;8:230-41. [DOI: 10.1016/j.jasc.2019.03.003] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 8.3] [Reference Citation Analysis]
54 De Togni G, Erikainen S, Chan S, Cunningham-Burley S. What makes AI 'intelligent' and 'caring'? Exploring affect and relationality across three sites of intelligence and care. Soc Sci Med 2021;277:113874. [PMID: 33901725 DOI: 10.1016/j.socscimed.2021.113874] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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56 Hu-Lieskovan S, Bhaumik S, Dhodapkar K, Grivel JJB, Gupta S, Hanks BA, Janetzki S, Kleen TO, Koguchi Y, Lund AW, Maccalli C, Mahnke YD, Novosiadly RD, Selvan SR, Sims T, Zhao Y, Maecker HT. SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery. J Immunother Cancer 2020;8:e000705. [PMID: 33268350 DOI: 10.1136/jitc-2020-000705] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
57 Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study. J Med Internet Res 2021;23:e25856. [PMID: 34842535 DOI: 10.2196/25856] [Reference Citation Analysis]
58 Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J 2020;18:2300-11. [PMID: 32994889 DOI: 10.1016/j.csbj.2020.08.019] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
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61 Santo BA, Rosenberg AZ, Sarder P. Artificial intelligence driven next-generation renal histomorphometry. Curr Opin Nephrol Hypertens 2020;29:265-72. [PMID: 32205581 DOI: 10.1097/MNH.0000000000000598] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
62 Anderson M, Pitchforth E, Asaria M, Brayne C, Casadei B, Charlesworth A, Coulter A, Franklin BD, Donaldson C, Drummond M, Dunnell K, Foster M, Hussey R, Johnson P, Johnston-Webber C, Knapp M, Lavery G, Longley M, Clark JM, Majeed A, McKee M, Newton JN, O'Neill C, Raine R, Richards M, Sheikh A, Smith P, Street A, Taylor D, Watt RG, Whyte M, Woods M, McGuire A, Mossialos E. LSE-Lancet Commission on the future of the NHS: re-laying the foundations for an equitable and efficient health and care service after COVID-19. Lancet 2021;397:1915-78. [PMID: 33965070 DOI: 10.1016/S0140-6736(21)00232-4] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
63 Garberis I, Andre F, Lacroix-Triki M. L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2: Could artificial intelligence play a role in breast cancer diagnosis? – The example of HER2. Bull Cancer 2021;108:11S35-45. [PMID: 34969514 DOI: 10.1016/S0007-4551(21)00635-4] [Reference Citation Analysis]
64 Chen P, El Hussein S, Xing F, Aminu M, Kannapiran A, Hazle JD, Medeiros LJ, Wistuba II, Jaffray D, Khoury JD, Wu J. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering. Cancers 2022;14:2398. [DOI: 10.3390/cancers14102398] [Reference Citation Analysis]
65 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]
66 Tserevelakis GJ, Mavrakis KG, Pantazopoulou D, Lagoudaki E, Detorakis E, Zacharakis G. Hybrid autofluorescence and photoacoustic label-free microscopy for the investigation and identification of malignancies in ocular biopsies. Opt Lett 2020;45:5748-51. [PMID: 33057275 DOI: 10.1364/OL.403435] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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