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For: Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 2019;25:1054-6. [PMID: 31160815 DOI: 10.1038/s41591-019-0462-y] [Cited by in Crossref: 221] [Cited by in F6Publishing: 178] [Article Influence: 73.7] [Reference Citation Analysis]
Number Citing Articles
1 Vogel A, Bathon M, Saborowski A. Immunotherapies in clinical development for biliary tract cancer. Expert Opin Investig Drugs 2021;30:351-63. [PMID: 33382361 DOI: 10.1080/13543784.2021.1868437] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]
2 [DOI: 10.1101/554527] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
3 Zou XL, Ren Y, Feng DY, He XQ, Guo YF, Yang HL, Li X, Fang J, Li Q, Ye JJ, Han LQ, Zhang TT. A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study. PLoS One 2020;15:e0236378. [PMID: 32706807 DOI: 10.1371/journal.pone.0236378] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
4 Shenoy S. Cell plasticity in cancer: A complex interplay of genetic, epigenetic mechanisms and tumor micro-environment. Surg Oncol 2020;34:154-62. [PMID: 32891322 DOI: 10.1016/j.suronc.2020.04.017] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
5 Chen Y, Yang H, Cheng Z, Chen L, Peng S, Wang J, Yang M, Lin C, Chen Y, Wang Y, Huang L, Chen Y, Li W, Ke Z. A whole-slide image (WSI)-based immunohistochemical feature prediction system improves the subtyping of lung cancer. Lung Cancer 2022;165:18-27. [DOI: 10.1016/j.lungcan.2022.01.005] [Reference Citation Analysis]
6 Fridman WH, Miller I, Sautès-Fridman C, Byrne AT. Therapeutic Targeting of the Colorectal Tumor Stroma. Gastroenterology 2020;158:303-21. [PMID: 31622621 DOI: 10.1053/j.gastro.2019.09.045] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
7 Kutzner H, Jutzi TB, Krahl D, Krieghoff-Henning EI, Heppt MV, Hekler A, Schmitt M, Maron RCR, Fröhling S, von Kalle C, Brinker TJ. Overdiagnosis of melanoma - causes, consequences and solutions. J Dtsch Dermatol Ges 2020;18:1236-43. [PMID: 32841508 DOI: 10.1111/ddg.14233] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27(20): 2545-2575 [PMID: 34092975 DOI: 10.3748/wjg.v27.i20.2545] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
10 Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, Wu L, Huang Y, Liang C, Liu Z. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054. [PMID: 33039706 DOI: 10.1016/j.ebiom.2020.103054] [Cited by in Crossref: 11] [Cited by in F6Publishing: 16] [Article Influence: 5.5] [Reference Citation Analysis]
11 Zeng H, Chen L, Zhang M, Luo Y, Ma X. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol 2021:S0090-8258(21)00577-1. [PMID: 34275655 DOI: 10.1016/j.ygyno.2021.07.015] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Levine AB, Peng J, Farnell D, Nursey M, Wang Y, Naso JR, Ren H, Farahani H, Chen C, Chiu D, Talhouk A, Sheffield B, Riazy M, Ip PP, Parra-Herran C, Mills A, Singh N, Tessier-Cloutier B, Salisbury T, Lee J, Salcudean T, Jones SJ, Huntsman DG, Gilks CB, Yip S, Bashashati A. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks. J Pathol 2020;252:178-88. [PMID: 32686118 DOI: 10.1002/path.5509] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
13 Foersch S, Eckstein M, Wagner DC, Gach F, Woerl AC, Geiger J, Glasner C, Schelbert S, Schulz S, Porubsky S, Kreft A, Hartmann A, Agaimy A, Roth W. Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann Oncol 2021;32:1178-87. [PMID: 34139273 DOI: 10.1016/j.annonc.2021.06.007] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, Mahmood F. AI-based pathology predicts origins for cancers of unknown primary. Nature 2021;594:106-10. [PMID: 33953404 DOI: 10.1038/s41586-021-03512-4] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
15 Park J, Chung YR, Kong ST, Kim YW, Park H, Kim K, Kim DI, Jung KH. Aggregation of cohorts for histopathological diagnosis with deep morphological analysis. Sci Rep 2021;11:2876. [PMID: 33536550 DOI: 10.1038/s41598-021-82642-1] [Reference Citation Analysis]
16 Woerl AC, Eckstein M, Geiger J, Wagner DC, Daher T, Stenzel P, Fernandez A, Hartmann A, Wand M, Roth W, Foersch S. Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides. Eur Urol 2020;78:256-64. [PMID: 32354610 DOI: 10.1016/j.eururo.2020.04.023] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
17 Yu S, Li H, Li X, Fu YV, Liu F. Classification of pathogens by Raman spectroscopy combined with generative adversarial networks. Science of The Total Environment 2020;726:138477. [DOI: 10.1016/j.scitotenv.2020.138477] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
18 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]
19 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]
20 Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol 2021;339:113608. [PMID: 33513353 DOI: 10.1016/j.expneurol.2021.113608] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Rakha EA, Alsaleem M, ElSharawy KA, Toss MS, Raafat S, Mihai R, Minhas FA, Green AR, Rajpoot NM, Dalton LW, Mongan NP. Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study. Histopathology 2020;77:631-45. [PMID: 32618014 DOI: 10.1111/his.14199] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
22 Bülow RD, Kers J, Boor P. Multistain segmentation of renal histology: first steps toward artificial intelligence-augmented digital nephropathology. Kidney Int 2021;99:17-9. [PMID: 33390226 DOI: 10.1016/j.kint.2020.08.025] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather JN, Gaiser T, Fröhling S, Brinker TJ. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur J Cancer 2022;160:80-91. [PMID: 34810047 DOI: 10.1016/j.ejca.2021.10.007] [Reference Citation Analysis]
24 Ahmedt-aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology. Computerized Medical Imaging and Graphics 2022;95:102027. [DOI: 10.1016/j.compmedimag.2021.102027] [Reference Citation Analysis]
25 Shen Y, Ke J. A Deformable CRF Model for Histopathology Whole-Slide Image Classification. In: Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Cham: Springer International Publishing; 2020. pp. 500-8. [DOI: 10.1007/978-3-030-59722-1_48] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
26 Tavolara TE, Niazi MKK, Gower AC, Ginese M, Beamer G, Gurcan MN. Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice. EBioMedicine 2021;67:103388. [PMID: 34000621 DOI: 10.1016/j.ebiom.2021.103388] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Jiang Z, Liu H, Zhang S, Liu J, Wang W, Zang G, Meng B, Lin H, Quan J, Zou S, Yuan D, Wang X, Tian G, Lang J. A Novel Method for Microsatellite Instability Detection by Liquid Biopsy Based on Next-generation Sequencing. CBIO 2021;16:53-62. [DOI: 10.2174/1574893615666200324133451] [Reference Citation Analysis]
28 Kalpathy-Cramer J, Patel JB, Bridge C, Chang K. Basic Artificial Intelligence Techniques: Evaluation of Artificial Intelligence Performance. Radiol Clin North Am 2021;59:941-54. [PMID: 34689879 DOI: 10.1016/j.rcl.2021.06.005] [Reference Citation Analysis]
29 Bilal M, Raza SEA, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit Health 2021;3:e763-72. [PMID: 34686474 DOI: 10.1016/S2589-7500(21)00180-1] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
30 Tan W, Guan P, Wu L, Chen H, Li J, Ling Y, Fan T, Wang Y, Li J, Yan B. The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography. Ann Transl Med 2021;9:969. [PMID: 34277769 DOI: 10.21037/atm-21-1171] [Reference Citation Analysis]
31 Lino-Silva LS, Xinaxtle DL. Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract. Future Oncol 2020;16:2845-51. [PMID: 32892631 DOI: 10.2217/fon-2020-0678] [Reference Citation Analysis]
32 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
33 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]
34 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]
35 Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N, Seibel T, Gray R, Hutchins GGA, Brenner H, van Treeck M, Yuan T, Brinker TJ, Chang-Claude J, Khader F, Schuppert A, Luedde T, Trautwein C, Muti HS, Foersch S, Hoffmeister M, Truhn D, Kather JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat Med 2022. [PMID: 35469069 DOI: 10.1038/s41591-022-01768-5] [Reference Citation Analysis]
36 Hashemzadeh H, Shojaeilangari S, Allahverdi A, Rothbauer M, Ertl P, Naderi-Manesh H. A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications. Sci Rep 2021;11:9804. [PMID: 33963232 DOI: 10.1038/s41598-021-89352-8] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
37 Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, Robinson BD, Eklund L, Jambor I, Merisaari H, Ettala O, Taimen P, Aronen HJ, Boström PJ, Tewari A, Magi-Galluzzi C, Klein E, Purysko A, Nc Shih N, Feldman M, Gupta S, Lal P, Madabhushi A. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. NPJ Precis Oncol 2021;5:35. [PMID: 33941830 DOI: 10.1038/s41698-021-00174-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
38 Bustos A, Payá A, Torrubia A, Jover R, Llor X, Bessa X, Castells A, Carracedo Á, Alenda C. xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer. Biomolecules 2021;11:1786. [DOI: 10.3390/biom11121786] [Reference Citation Analysis]
39 Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 2021;124:686-96. [PMID: 33204028 DOI: 10.1038/s41416-020-01122-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
40 Howard FM, Dolezal J, Kochanny S, Schulte J, Chen H, Heij L, Huo D, Nanda R, Olopade OI, Kather JN, Cipriani N, Grossman RL, Pearson AT. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun 2021;12:4423. [PMID: 34285218 DOI: 10.1038/s41467-021-24698-1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
41 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
42 Imamura K, Yada Y, Izumi Y, Morita M, Kawata A, Arisato T, Nagahashi A, Enami T, Tsukita K, Kawakami H, Nakagawa M, Takahashi R, Inoue H. Prediction Model of Amyotrophic Lateral Sclerosis by Deep Learning with Patient Induced Pluripotent Stem Cells. Ann Neurol 2021;89:1226-33. [PMID: 33565152 DOI: 10.1002/ana.26047] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
43 Echle A, Ghaffari Laleh N, Quirke P, Grabsch HI, Muti HS, Saldanha OL, Brockmoeller SF, van den Brandt PA, Hutchins GGA, Richman SD, Horisberger K, Galata C, Ebert MP, Eckardt M, Boutros M, Horst D, Reissfelder C, Alwers E, Brinker TJ, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Chang-Claude J, Brenner H, Trautwein C, Boor P, Jaeger D, Gaisa NT, Hoffmeister M, West NP, Kather JN. Artificial intelligence for detection of microsatellite instability in colorectal cancer-a multicentric analysis of a pre-screening tool for clinical application. ESMO Open 2022;7:100400. [PMID: 35247870 DOI: 10.1016/j.esmoop.2022.100400] [Reference Citation Analysis]
44 Okada M, Shimizu K, Fujii S. Identification of Neoantigens in Cancer Cells as Targets for Immunotherapy. IJMS 2022;23:2594. [DOI: 10.3390/ijms23052594] [Reference Citation Analysis]
45 Boor P. Artificial intelligence in nephropathology. Nat Rev Nephrol 2020;16:4-6. [DOI: 10.1038/s41581-019-0220-x] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.7] [Reference Citation Analysis]
46 . Computational pathology and the understanding of disease. J Pathol 2019;249:141-2. [PMID: 31403171 DOI: 10.1002/path.5337] [Reference Citation Analysis]
47 Sneider A, Kiemen A, Kim JH, Wu P, Habibi M, White M, Phillip JM, Gu L, Wirtz D. Deep learning identification of stiffness markers in breast cancer. Biomaterials 2022;285:121540. [DOI: 10.1016/j.biomaterials.2022.121540] [Reference Citation Analysis]
48 El Hussein S, Chen P, Medeiros LJ, Wistuba II, Jaffray D, Wu J, Khoury JD. Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia. J Pathol 2021. [PMID: 34505705 DOI: 10.1002/path.5795] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Sandeman K, Blom S, Koponen V, Manninen A, Juhila J, Rannikko A, Ropponen T, Mirtti T. AI Model for Prostate Biopsies Predicts Cancer Survival. Diagnostics 2022;12:1031. [DOI: 10.3390/diagnostics12051031] [Reference Citation Analysis]
50 Li L, Feng Q, Wang X. PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer. Comput Struct Biotechnol J 2020;18:668-75. [PMID: 32257050 DOI: 10.1016/j.csbj.2020.03.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 10] [Article Influence: 3.5] [Reference Citation Analysis]
51 Nguyen HG, Blank A, Dawson HE, Lugli A, Zlobec I. Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods. Sci Rep 2021;11:2371. [PMID: 33504830 DOI: 10.1038/s41598-021-81352-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2020;24:89-99. [PMID: 31908578 DOI: 10.4196/kjpp.2020.24.1.89] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
53 Gilson P, Merlin JL, Harlé A. Detection of Microsatellite Instability: State of the Art and Future Applications in Circulating Tumour DNA (ctDNA). Cancers (Basel) 2021;13:1491. [PMID: 33804907 DOI: 10.3390/cancers13071491] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
54 Garland J, Hu M, Kesha K, Glenn C, Morrow P, Stables S, Ondruschka B, Tse R. Identifying gross post-mortem organ images using a pre-trained convolutional neural network. J Forensic Sci 2021;66:630-5. [PMID: 33105027 DOI: 10.1111/1556-4029.14608] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
55 Murchan P, Ó'Brien C, O'Connell S, McNevin CS, Baird AM, Sheils O, Ó Broin P, Finn SP. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics. Diagnostics (Basel) 2021;11:1406. [PMID: 34441338 DOI: 10.3390/diagnostics11081406] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
56 Yamamoto H, Watanabe Y, Maehata T, Imai K, Itoh F. Microsatellite instability in cancer: a novel landscape for diagnostic and therapeutic approach. Arch Toxicol 2020;94:3349-57. [DOI: 10.1007/s00204-020-02833-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
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58 Goertzen N, Pappesch R, Fassunke J, Brüning T, Ko YD, Schmidt J, Großerueschkamp F, Buettner R, Gerwert K. Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma. Am J Pathol 2021;191:1269-80. [PMID: 34004158 DOI: 10.1016/j.ajpath.2021.04.013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 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]
60 Du Y, Han D, Liu S, Sun X, Ning B, Han T, Wang J, Gao Z. Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria. Talanta 2022;237:122901. [PMID: 34736716 DOI: 10.1016/j.talanta.2021.122901] [Reference Citation Analysis]
61 Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27(27): 4395-4412 [PMID: 34366612 DOI: 10.3748/wjg.v27.i27.4395] [Reference Citation Analysis]
62 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]
63 McInnes G, Sharo AG, Koleske ML, Brown JEH, Norstad M, Adhikari AN, Wang S, Brenner SE, Halpern J, Koenig BA, Magnus DC, Gallagher RC, Giacomini KM, Altman RB. Opportunities and challenges for the computational interpretation of rare variation in clinically important genes. Am J Hum Genet 2021;108:535-48. [PMID: 33798442 DOI: 10.1016/j.ajhg.2021.03.003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
64 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]
65 Chen Q, Kuai Y, Wang S, Zhu X, Wang H, Liu W, Cheng L, Yang D. Deep Learning–Based Classification of Epithelial–Mesenchymal Transition for Predicting Response to Therapy in Clear Cell Renal Cell Carcinoma. Front Oncol 2022;11:782515. [DOI: 10.3389/fonc.2021.782515] [Reference Citation Analysis]
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