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For: Serag A, Ion-Margineanu A, Qureshi H, McMillan R, Saint Martin MJ, Diamond J, O'Reilly P, Hamilton P. Translational AI and Deep Learning in Diagnostic Pathology. Front Med (Lausanne). 2019;6:185. [PMID: 31632973 DOI: 10.3389/fmed.2019.00185] [Cited by in Crossref: 56] [Cited by in F6Publishing: 49] [Article Influence: 18.7] [Reference Citation Analysis]
Number Citing Articles
1 Hou J, Nast CC. Artificial Intelligence: The Next Frontier in Kidney Biopsy Evaluation. Clin J Am Soc Nephrol 2020;15:1389-91. [PMID: 32938618 DOI: 10.2215/CJN.13450820] [Reference Citation Analysis]
2 Roy RM, Ameer PM. Identification of white blood cells for the diagnosis of acute myeloid leukemia. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22702] [Reference Citation Analysis]
3 Aoyama Y, Maruko I, Kawano T, Yokoyama T, Ogawa Y, Maruko R, Iida T. Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature: A pilot study. PLoS One 2021;16:e0244469. [PMID: 34143775 DOI: 10.1371/journal.pone.0244469] [Reference Citation Analysis]
4 López F, Mäkitie A, de Bree R, Franchi A, de Graaf P, Hernández-Prera JC, Strojan P, Zidar N, Strojan Fležar M, Rodrigo JP, Rinaldo A, Centeno BA, Ferlito A. Qualitative and Quantitative Diagnosis in Head and Neck Cancer. Diagnostics (Basel) 2021;11:1526. [PMID: 34573868 DOI: 10.3390/diagnostics11091526] [Reference Citation Analysis]
5 Lee K, Lockhart JH, Xie M, Chaudhary R, Slebos RJC, Flores ER, Chung CH, Tan AC. Deep Learning of Histopathology Images at the Single Cell Level. Front Artif Intell 2021;4:754641. [PMID: 34568816 DOI: 10.3389/frai.2021.754641] [Reference Citation Analysis]
6 Koga S, Ikeda A, Dickson DW. Deep learning-based model for diagnosing Alzheimer's disease and tauopathies. Neuropathol Appl Neurobiol 2021. [PMID: 34402107 DOI: 10.1111/nan.12759] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 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]
8 Koga S, Ghayal NB, Dickson DW. Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques. J Neuropathol Exp Neurol 2021;80:306-12. [PMID: 33570124 DOI: 10.1093/jnen/nlab005] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 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]
10 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]
11 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]
12 Kerner J, Dogan A, von Recum H. Machine learning and big data provide crucial insight for future biomaterials discovery and research. Acta Biomater 2021;130:54-65. [PMID: 34087445 DOI: 10.1016/j.actbio.2021.05.053] [Reference Citation Analysis]
13 Pocevičiūtė M, Eilertsen G, Lundström C. Survey of XAI in Digital Pathology. In: Holzinger A, Goebel R, Mengel M, Müller H, editors. Artificial Intelligence and Machine Learning for Digital Pathology. Cham: Springer International Publishing; 2020. pp. 56-88. [DOI: 10.1007/978-3-030-50402-1_4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
14 Ghoshal UC, Rai S, Kulkarni A, Gupta A. Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence. JGH Open. 2020;4:889-897. [PMID: 33102760 DOI: 10.1002/jgh3.12342] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
15 Bazoukis G, Stavrakis S, Zhou J, Bollepalli SC, Tse G, Zhang Q, Singh JP, Armoundas AA. Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review. Heart Fail Rev 2021;26:23-34. [PMID: 32720083 DOI: 10.1007/s10741-020-10007-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
16 Laivuori M, Tolva J, Lokki AI, Linder N, Lundin J, Paakkanen R, Albäck A, Venermo M, Mäyränpää MI, Lokki ML, Sinisalo J. Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men. Front Cardiovasc Med 2020;7:594192. [PMID: 33363220 DOI: 10.3389/fcvm.2020.594192] [Reference Citation Analysis]
17 Braun M, Piasecka D, Bobrowski M, Kordek R, Sadej R, Romanska HM. A 'Real-Life' Experience on Automated Digital Image Analysis of FGFR2 Immunohistochemistry in Breast Cancer. Diagnostics (Basel) 2020;10:E1060. [PMID: 33297384 DOI: 10.3390/diagnostics10121060] [Reference Citation Analysis]
18 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]
19 Muñoz-Aguirre M, Ntasis VF, Rojas S, Guigó R. PyHIST: A Histological Image Segmentation Tool. PLoS Comput Biol 2020;16:e1008349. [PMID: 33075075 DOI: 10.1371/journal.pcbi.1008349] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Oster HS, Crouch S, Smith A, Yu G, Abu Shrkihe B, Baruch S, Kolomansky A, Ben-Ezra J, Naor S, Fenaux P, Symeonidis A, Stauder R, Cermak J, Sanz G, Hellström-Lindberg E, Malcovati L, Langemeijer S, Germing U, Holm MS, Madry K, Guerci-Bresler A, Culligan D, Sanhes L, Mills J, Kotsianidis I, van Marrewijk C, Bowen D, de Witte T, Mittelman M. A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS. Blood Adv 2021;5:3066-75. [PMID: 34387647 DOI: 10.1182/bloodadvances.2020004055] [Reference Citation Analysis]
21 Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O'Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021;49:815-42. [PMID: 33618634 DOI: 10.1177/0192623320986423] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 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]
23 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]
24 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]
25 Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimed Tools Appl 2021;:1-34. [PMID: 33841033 DOI: 10.1007/s11042-021-10707-4] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
26 Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology 2021;78:791-804. [PMID: 33211332 DOI: 10.1111/his.14304] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
27 Dudgeon SN, Wen S, Hanna MG, Gupta R, Amgad M, Sheth M, Marble H, Huang R, Herrmann MD, Szu CH, Tong D, Werness B, Szu E, Larsimont D, Madabhushi A, Hytopoulos E, Chen W, Singh R, Hart SN, Sharma A, Saltz J, Salgado R, Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform 2021;12:45. [PMID: 34881099 DOI: 10.4103/jpi.jpi_83_20] [Reference Citation Analysis]
28 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]
29 Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ Jr. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021. [PMID: 34397396 DOI: 10.3233/BD-201011] [Reference Citation Analysis]
30 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 [PMID: 33177794 DOI: 10.3748/wjg.v26.i40.6207] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
31 Aloqaily A, Polonia A, Campelos S, Alrefae N, Vale J, Caramelo A, Eloy C. Digital Versus Optical Diagnosis of Follicular Patterned Thyroid Lesions. Head Neck Pathol 2021;15:537-43. [PMID: 33128731 DOI: 10.1007/s12105-020-01243-y] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
32 Chang TC, Seufert C, Eminaga O, Shkolyar E, Hu JC, Liao JC. Current Trends in Artificial Intelligence Application for Endourology and Robotic Surgery. Urol Clin North Am 2021;48:151-60. [PMID: 33218590 DOI: 10.1016/j.ucl.2020.09.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
33 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]
34 Paranjape K, Schinkel M, Hammer RD, Schouten B, Nannan Panday RS, Elbers PWG, Kramer MHH, Nanayakkara P. The Value of Artificial Intelligence in Laboratory Medicine. Am J Clin Pathol 2021;155:823-31. [PMID: 33313667 DOI: 10.1093/ajcp/aqaa170] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 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]
36 Yim K, Shin JH, Yoo J. Novel Pathologic Factors for Risk Stratification of Gastric "Indefinite for Dysplasia" Lesions. Gastroenterol Res Pract 2020;2020:9460681. [PMID: 33061961 DOI: 10.1155/2020/9460681] [Reference Citation Analysis]
37 Marsh JN, Liu TC, Wilson PC, Swamidass SJ, Gaut JP. Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens. JAMA Netw Open 2021;4:e2030939. [PMID: 33471115 DOI: 10.1001/jamanetworkopen.2020.30939] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
38 Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, Muhammad M, Khurram SA. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol 2021;131:711-20. [PMID: 32950425 DOI: 10.1016/j.oooo.2020.08.024] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
39 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]
40 Sargolzaei S. Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Front Comput Neurosci 2021;15:670489. [PMID: 34025380 DOI: 10.3389/fncom.2021.670489] [Reference Citation Analysis]
41 Rodrigues MA, Probst CE, Zayats A, Davidson B, Riedel M, Li Y, Venkatachalam V. The in vitro micronucleus assay using imaging flow cytometry and deep learning. NPJ Syst Biol Appl 2021;7:20. [PMID: 34006858 DOI: 10.1038/s41540-021-00179-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
42 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]
43 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]
44 Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors (Basel) 2021;21:2586. [PMID: 33917035 DOI: 10.3390/s21082586] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Höhn J, Krieghoff-Henning E, Jutzi TB, von Kalle C, Utikal JS, Meier F, Gellrich FF, Hobelsberger S, Hauschild A, Schlager JG, French L, Heinzerling L, Schlaak M, Ghoreschi K, Hilke FJ, Poch G, Kutzner H, Heppt MV, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Goebeler M, Hekler A, Fröhling S, Lipka DB, Kather JN, Krahl D, Ferrara G, Haggenmüller S, Brinker TJ. Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification. Eur J Cancer 2021;149:94-101. [PMID: 33838393 DOI: 10.1016/j.ejca.2021.02.032] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
46 Rudmann D, Albretsen J, Doolan C, Gregson M, Dray B, Sargeant A, O'Shea D D, Kuklyte J, Power A, Fitzgerald J. Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies. Toxicol Pathol 2021;49:938-49. [PMID: 33287665 DOI: 10.1177/0192623320973986] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
47 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]
48 Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am 2021;35:279-93. [PMID: 33641869 DOI: 10.1016/j.hoc.2021.01.002] [Reference Citation Analysis]
49 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]
50 Alaidarous MA. The emergence of new trends in clinical laboratory diagnosis. Saudi Med J 2020;41:1175-80. [PMID: 33130836 DOI: 10.15537/smj.2020.11.25455] [Reference Citation Analysis]
51 Korngiebel DM, Mooney SD. Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery. NPJ Digit Med 2021;4:93. [PMID: 34083689 DOI: 10.1038/s41746-021-00464-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Vuong TTL, Kim K, Song B, Kwak JT. Joint categorical and ordinal learning for cancer grading in pathology images. Med Image Anal 2021;73:102206. [PMID: 34399153 DOI: 10.1016/j.media.2021.102206] [Reference Citation Analysis]