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Cited by in F6Publishing
For: Colling R, Pitman H, Oien K, Rajpoot N, Macklin P;  CM-Path AI in Histopathology Working Group; Snead D, Sackville T, Verrill C. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol. 2019;249:143-150. [PMID: 31144302 DOI: 10.1002/path.5310] [Cited by in Crossref: 59] [Cited by in F6Publishing: 43] [Article Influence: 19.7] [Reference Citation Analysis]
Number Citing Articles
1 Meyerholz DK, Beck AP. Histopathologic Evaluation and Scoring of Viral Lung Infection. Methods Mol Biol 2020;2099:205-20. [PMID: 31883098 DOI: 10.1007/978-1-0716-0211-9_16] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
2 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]
3 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]
4 Karabekmez ME. Data Ethics in Digital Health and Genomics. New Bioeth 2021;27:320-33. [PMID: 34747348 DOI: 10.1080/20502877.2021.1996965] [Reference Citation Analysis]
5 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]
6 . Computational pathology and the understanding of disease. J Pathol 2019;249:141-2. [PMID: 31403171 DOI: 10.1002/path.5337] [Reference Citation Analysis]
7 Huss R, Coupland SE. Software‐assisted decision support in digital histopathology. J Pathol 2020;250:685-92. [DOI: 10.1002/path.5388] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 7.5] [Reference Citation Analysis]
8 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]
9 Ghosh A, Sirinukunwattana K, Khalid Alham N, Browning L, Colling R, Protheroe A, Protheroe E, Jones S, Aberdeen A, Rittscher J, Verrill C. The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer. Cancers (Basel) 2021;13:1325. [PMID: 33809521 DOI: 10.3390/cancers13061325] [Reference Citation Analysis]
10 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]
11 Bull JA, Macklin PS, Quaiser T, Braun F, Waters SL, Pugh CW, Byrne HM. Combining multiple spatial statistics enhances the description of immune cell localisation within tumours. Sci Rep 2020;10:18624. [PMID: 33122646 DOI: 10.1038/s41598-020-75180-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
12 Mittal S, Wrobel TP, Walsh M, Kajdacsy-balla A, Bhargava R. Breast cancer histopathology using infrared spectroscopic imaging: The impact of instrumental configurations. Clinical Spectroscopy 2021;3:100006. [DOI: 10.1016/j.clispe.2021.100006] [Reference Citation Analysis]
13 Browning L, Colling R, Rakha E, Rajpoot N, Rittscher J, James JA, Salto-Tellez M, Snead DRJ, Verrill C. Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective. J Clin Pathol. 2020;. [PMID: 32620678 DOI: 10.1136/jclinpath-2020-206854] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
14 Acs B, Hartman J. Next generation pathology: artificial intelligence enhances histopathology practice. J Pathol 2020;250:7-8. [DOI: 10.1002/path.5343] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
15 Betmouni S. Diagnostic digital pathology implementation: Learning from the digital health experience. Digit Health 2021;7:20552076211020240. [PMID: 34211723 DOI: 10.1177/20552076211020240] [Reference Citation Analysis]
16 Steiner DF, Chen PC, Mermel CH. Closing the translation gap: AI applications in digital pathology. Biochim Biophys Acta Rev Cancer 2021;1875:188452. [PMID: 33065195 DOI: 10.1016/j.bbcan.2020.188452] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Chatrian A, Colling RT, Browning L, Alham NK, Sirinukunwattana K, Malacrino S, Haghighat M, Aberdeen A, Monks A, Moxley-Wyles B, Rakha E, Snead DRJ, Rittscher J, Verrill C. Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies. Mod Pathol 2021;34:1780-94. [PMID: 34017063 DOI: 10.1038/s41379-021-00826-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
18 Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 DOI: 10.4103/jpi.jpi_27_20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
19 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]
20 Sorell T, Rajpoot N, Verrill C. Ethical issues in computational pathology. J Med Ethics 2021:medethics-2020-107024. [PMID: 33658334 DOI: 10.1136/medethics-2020-107024] [Reference Citation Analysis]
21 Krishnan SR, Bung N, Bulusu G, Roy A. Accelerating De Novo Drug Design against Novel Proteins Using Deep Learning. J Chem Inf Model 2021;61:621-30. [PMID: 33491455 DOI: 10.1021/acs.jcim.0c01060] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
22 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288:62-81. [PMID: 32128929 DOI: 10.1111/joim.13030] [Cited by in Crossref: 46] [Cited by in F6Publishing: 44] [Article Influence: 23.0] [Reference Citation Analysis]
24 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]
25 Tătaru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021;11:354. [PMID: 33672608 DOI: 10.3390/diagnostics11020354] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
26 Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021;12:13. [PMID: 34012717 DOI: 10.4103/jpi.jpi_84_20] [Reference Citation Analysis]
27 Inge L, Dennis E. Development and applications of computer image analysis algorithms for scoring of PD-L1 immunohistochemistry. Immuno-Oncology Technology 2020;6:2-8. [DOI: 10.1016/j.iotech.2020.04.001] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
28 Yuan HX, Yu QH, Zhang YQ, Yu Q, Zhang Q, Wang WP. Ultrasound Radiomics Effective for Preoperative Identification of True and Pseudo Gallbladder Polyps Based on Spatial and Morphological Features. Front Oncol 2020;10:1719. [PMID: 33042816 DOI: 10.3389/fonc.2020.01719] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
29 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]
30 Xie X, Wang X, Liang Y, Yang J, Wu Y, Li L, Sun X, Bing P, He B, Tian G, Shi X. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review. Front Oncol 2021;11:763527. [PMID: 34900711 DOI: 10.3389/fonc.2021.763527] [Reference Citation Analysis]
31 Ryu HS, Jin MS, Park JH, Lee S, Cho J, Oh S, Kwak TY, Woo JI, Mun Y, Kim SW, Hwang S, Shin SJ, Chang H. Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment. Cancers (Basel) 2019;11:E1860. [PMID: 31769420 DOI: 10.3390/cancers11121860] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
32 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]
33 Prade VM, Kunzke T, Feuchtinger A, Rohm M, Luber B, Lordick F, Buck A, Walch A. De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry. Mol Metab 2020;36:100953. [PMID: 32278304 DOI: 10.1016/j.molmet.2020.01.017] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
34 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]
35 Fleming KA, Horton S, Wilson ML, Atun R, DeStigter K, Flanigan J, Sayed S, Adam P, Aguilar B, Andronikou S, Boehme C, Cherniak W, Cheung AN, Dahn B, Donoso-Bach L, Douglas T, Garcia P, Hussain S, Iyer HS, Kohli M, Labrique AB, Looi LM, Meara JG, Nkengasong J, Pai M, Pool KL, Ramaiya K, Schroeder L, Shah D, Sullivan R, Tan BS, Walia K. The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet 2021;398:1997-2050. [PMID: 34626542 DOI: 10.1016/S0140-6736(21)00673-5] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
36 Palumbo B, Bianconi F, Nuvoli S, Spanu A, Fravolini ML. Artificial intelligence techniques support nuclear medicine modalities to improve the diagnosis of Parkinson’s disease and Parkinsonian syndromes. Clin Transl Imaging 2021;9:19-35. [DOI: 10.1007/s40336-020-00404-x] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
37 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
38 Moxley-wyles B, Colling R, Verrill C. Artificial intelligence in pathology: an overview. Diagnostic Histopathology 2020;26:513-20. [DOI: 10.1016/j.mpdhp.2020.08.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
39 Macklin PS, Pillay N, Lee JL, Pitman H, Scott S, Wang J, Craig C, Jones JL, Oien KA, Colling R, Coupland SE, Verrill C; CM-Path Molecular Diagnostics working group. CM-Path Molecular Diagnostics Forum-consensus statement on the development and implementation of molecular diagnostic tests in the United Kingdom. Br J Cancer 2019;121:738-43. [PMID: 31575975 DOI: 10.1038/s41416-019-0588-1] [Reference Citation Analysis]
40 Classe M, Lerousseau M, Scoazec JY, Deutsch E. Perspectives in pathomics in head and neck cancer. Curr Opin Oncol 2021;33:175-83. [PMID: 33782358 DOI: 10.1097/CCO.0000000000000731] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
41 Jones-Hall Y. Digital pathology in academia: Implementation and impact. Lab Anim (NY) 2021;50:229-31. [PMID: 34349254 DOI: 10.1038/s41684-021-00828-6] [Reference Citation Analysis]
42 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]
43 Liu S, Zhang Y, Ju Y, Li Y, Kang X, Yang X, Niu T, Xing X, Lu Y. Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding. Front Oncol 2021;11:626626. [PMID: 33763362 DOI: 10.3389/fonc.2021.626626] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
44 Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol 2021;16:24. [PMID: 33731170 DOI: 10.1186/s13000-021-01085-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
45 Turnquist C, Roberts-Gant S, Hemsworth H, White K, Browning L, Rees G, Roskell D, Verrill C. On the Edge of a Digital Pathology Transformation: Views from a Cellular Pathology Laboratory Focus Group. J Pathol Inform 2019;10:37. [PMID: 31897354 DOI: 10.4103/jpi.jpi_38_19] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
46 Colling R, Protheroe A, Sullivan M, Macpherson R, Tuthill M, Redgwell J, Traill Z, Molyneux A, Johnson E, Abdullah N, Taibi A, Mercer N, Haynes HR, Sackville A, Craft J, Reis J, Rees G, Soares M, Roberts ISD, Siiankoski D, Hemsworth H, Roskell D, Roberts-gant S, White K, Rittscher J, Davies J, Browning L, Verrill C. Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network. Diagnostics 2021;11:2191. [DOI: 10.3390/diagnostics11122191] [Reference Citation Analysis]
47 Schlitter AM, Häberle L, Richter C, Huss R, Esposito I. [Standardized diagnosis of pancreatic head carcinoma]. Pathologe 2021;42:453-63. [PMID: 34357472 DOI: 10.1007/s00292-021-00971-4] [Reference Citation Analysis]
48 Delaune A, Valmary-degano S, Loménie N, Zryouil K, Benyahia N, Trassard O, Eraville V, Bergeron C, Devouassoux-shisheboran M, Glaser C, Bataillon G, Bacry E, Combes S, Prevot S, Bertheau P. Le premier data challenge organisé par la Société Française de Pathologie : une compétition internationale en 2020, un outil de recherche en intelligence artificielle pour l’avenir ? Annales de Pathologie 2022. [DOI: 10.1016/j.annpat.2021.10.002] [Reference Citation Analysis]
49 Hvid H, Skydsgaard M, Jensen NK, Viuff BM, Jensen HE, Oleksiewicz MB, Kvist PH. Artificial Intelligence-Based Quantification of Epithelial Proliferation in Mammary Glands of Rats and Oviducts of Göttingen Minipigs. Toxicol Pathol 2021;49:912-27. [PMID: 32840183 DOI: 10.1177/0192623320950633] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]