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For: Levine AB, Schlosser C, Grewal J, Coope R, Jones SJM, Yip S. Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis. Trends Cancer 2019;5:157-69. [PMID: 30898263 DOI: 10.1016/j.trecan.2019.02.002] [Cited by in Crossref: 44] [Cited by in F6Publishing: 33] [Article Influence: 14.7] [Reference Citation Analysis]
Number Citing Articles
1 Chea P, Mandell JC. Current applications and future directions of deep learning in musculoskeletal radiology. Skeletal Radiol 2020;49:183-97. [PMID: 31377836 DOI: 10.1007/s00256-019-03284-z] [Cited by in Crossref: 19] [Cited by in F6Publishing: 13] [Article Influence: 6.3] [Reference Citation Analysis]
2 Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, Elyan E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? Sensors (Basel) 2021;21:5526. [PMID: 34450976 DOI: 10.3390/s21165526] [Reference Citation Analysis]
3 Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Cited by in Crossref: 43] [Cited by in F6Publishing: 32] [Article Influence: 14.3] [Reference Citation Analysis]
4 Capobianco E. High-dimensional role of AI and machine learning in cancer research. Br J Cancer. [DOI: 10.1038/s41416-021-01689-z] [Reference Citation Analysis]
5 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]
6 Zimmerman L, Zelichov O, Aizenmann A, Barbash Z, Vidne M, Tarcic G. A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks. Sci Rep 2020;10:4192. [PMID: 32144301 DOI: 10.1038/s41598-020-61173-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
7 Ji Z, Gong J, Feng J, Huang J. A Novel Deep Learning Approach for Anomaly Detection of Time Series Data. Scientific Programming 2021;2021:1-11. [DOI: 10.1155/2021/6636270] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Paijens ST, Vledder A, Loiero D, Duiker EW, Bart J, Hendriks AM, Jalving M, Workel HH, Hollema H, Werner N, Plat A, Wisman GBA, Yigit R, Arts H, Kruse AJ, de Lange NM, Koelzer VH, de Bruyn M, Nijman HW. Prognostic image-based quantification of CD8CD103 T cell subsets in high-grade serous ovarian cancer patients. Oncoimmunology 2021;10:1935104. [PMID: 34123576 DOI: 10.1080/2162402X.2021.1935104] [Reference Citation Analysis]
9 Shao D, Dai Y, Li N, Cao X, Zhao W, Cheng L, Rong Z, Huang L, Wang Y, Zhao J. Artificial intelligence in clinical research of cancers. Brief Bioinform 2021:bbab523. [PMID: 34929741 DOI: 10.1093/bib/bbab523] [Reference Citation Analysis]
10 Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-8. [PMID: 33721700 DOI: 10.1016/j.ejmp.2021.02.024] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Tsimberidou AM, Fountzilas E, Bleris L, Kurzrock R. Transcriptomics and solid tumors: The next frontier in precision cancer medicine. Semin Cancer Biol 2020:S1044-579X(20)30196-6. [PMID: 32950605 DOI: 10.1016/j.semcancer.2020.09.007] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
12 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]
13 Escalante A, González-Martínez R, Herrera E. New techniques for studying neurodevelopment. Fac Rev 2020;9:17. [PMID: 33659949 DOI: 10.12703/r/9-17] [Reference Citation Analysis]
14 Gu H, Zhang X, di Russo P, Zhao X, Xu T. The Current State of Radiomics for Meningiomas: Promises and Challenges. Front Oncol 2020;10:567736. [PMID: 33194649 DOI: 10.3389/fonc.2020.567736] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021;101:412-22. [PMID: 33454724 DOI: 10.1038/s41374-020-00514-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
16 Habuza T, Navaz AN, Hashim F, Alnajjar F, Zaki N, Serhani MA, Statsenko Y. AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine. Informatics in Medicine Unlocked 2021;24:100596. [DOI: 10.1016/j.imu.2021.100596] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
17 Deshpande S, Minhas F, Graham S, Rajpoot N. SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images. Med Image Anal 2021;77:102337. [PMID: 35016078 DOI: 10.1016/j.media.2021.102337] [Reference Citation Analysis]
18 Kim KM, Heo TY, Kim A, Kim J, Han KJ, Yun J, Min JK. Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases. J Pers Med 2021;11:321. [PMID: 33918998 DOI: 10.3390/jpm11050321] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Guerrero‐ibañez J, Contreras‐castillo J, Zeadally S. Deep learning support for intelligent transportation systems. Trans Emerging Tel Tech 2021;32. [DOI: 10.1002/ett.4169] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Zhang Y, Jiang H, Ye T, Juhas M. Deep Learning for Imaging and Detection of Microorganisms. Trends Microbiol 2021;29:569-72. [PMID: 33531192 DOI: 10.1016/j.tim.2021.01.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Heo TY, Kim KM, Min HK, Gu SM, Kim JH, Yun J, Min JK. Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration. Diagnostics (Basel) 2020;10:E261. [PMID: 32354098 DOI: 10.3390/diagnostics10050261] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Syrykh C, Abreu A, Amara N, Siegfried A, Maisongrosse V, Frenois FX, Martin L, Rossi C, Laurent C, Brousset P. Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning. NPJ Digit Med 2020;3:63. [PMID: 32377574 DOI: 10.1038/s41746-020-0272-0] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
23 Song J, Jiao W, Lankowicz K, Cai Z, Bi H. A two-stage adaptive thresholding segmentation for noisy low-contrast images. Ecological Informatics 2022. [DOI: 10.1016/j.ecoinf.2022.101632] [Reference Citation Analysis]
24 Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021;11:8027-42. [PMID: 34335978 DOI: 10.7150/thno.61207] [Reference Citation Analysis]
25 Melo RCN, Raas MWD, Palazzi C, Neves VH, Malta KK, Silva TP. Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders. Front Med (Lausanne) 2019;6:310. [PMID: 31970160 DOI: 10.3389/fmed.2019.00310] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
26 Simidjievski N, Bodnar C, Tariq I, Scherer P, Andres Terre H, Shams Z, Jamnik M, Liò P. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Front Genet 2019;10:1205. [PMID: 31921281 DOI: 10.3389/fgene.2019.01205] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 5.3] [Reference Citation Analysis]
27 Sompairac N, Nazarov PV, Czerwinska U, Cantini L, Biton A, Molkenov A, Zhumadilov Z, Barillot E, Radvanyi F, Gorban A, Kairov U, Zinovyev A. Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets. Int J Mol Sci 2019;20:E4414. [PMID: 31500324 DOI: 10.3390/ijms20184414] [Cited by in Crossref: 31] [Cited by in F6Publishing: 13] [Article Influence: 10.3] [Reference Citation Analysis]
28 Ünal HT, Başçiftçi F. Evolutionary design of neural network architectures: a review of three decades of research. Artif Intell Rev 2022;55:1723-802. [DOI: 10.1007/s10462-021-10049-5] [Reference Citation Analysis]
29 Dey P. The emerging role of deep learning in cytology. Cytopathology 2021;32:154-60. [PMID: 33222315 DOI: 10.1111/cyt.12942] [Reference Citation Analysis]
30 Zhang Y, Lei Y, Qiu RLJ, Wang T, Wang H, Jani AB, Curran WJ, Patel P, Liu T, Yang X. Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy. Med Phys 2020;47:2735-45. [PMID: 32155666 DOI: 10.1002/mp.14128] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
31 Han T, Nebelung S, Pedersoli F, Zimmermann M, Schulze-Hagen M, Ho M, Haarburger C, Kiessling F, Kuhl C, Schulz V, Truhn D. Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization. Nat Commun 2021;12:4315. [PMID: 34262044 DOI: 10.1038/s41467-021-24464-3] [Reference Citation Analysis]
32 Theek B, Magnuska Z, Gremse F, Hahn H, Schulz V, Kiessling F. Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice. Methods 2021;188:30-6. [PMID: 32615232 DOI: 10.1016/j.ymeth.2020.06.019] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
33 Lejeune M, Plancoulaine B, Elie N, Bosch R, Fontoura L, de Villasante I, Korzyńska A, Navarro AG, Colón ES, López C. How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction. Histochem Cell Biol 2021. [PMID: 34383240 DOI: 10.1007/s00418-021-02022-8] [Reference Citation Analysis]
34 Cui S, Tseng HH, Pakela J, Ten Haken RK, El Naqa I. Introduction to machine and deep learning for medical physicists. Med Phys 2020;47:e127-47. [PMID: 32418339 DOI: 10.1002/mp.14140] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 15.0] [Reference Citation Analysis]
35 Puvanasunthararajah S, Fontanarosa D, Wille ML, Camps SM. The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review. J Appl Clin Med Phys 2021;22:198-223. [PMID: 33938608 DOI: 10.1002/acm2.13255] [Reference Citation Analysis]
36 Yow AP, Srivastava R, Cheng J, Li A, Liu J, Schmetterer L, Tey HL, Wong DWK. Techniques and Applications in Skin OCT Analysis. Adv Exp Med Biol 2020;1213:149-63. [PMID: 32030669 DOI: 10.1007/978-3-030-33128-3_10] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. Patterns (N Y) 2021;2:100323. [PMID: 34553170 DOI: 10.1016/j.patter.2021.100323] [Reference Citation Analysis]
38 Barsasella D, Syed-Abdul S, Malwade S, Kuo TBJ, Chien MJ, Núñez-Benjumea FJ, Lai GM, Kao RH, Shih HJ, Wen YC, Li YJ, Carrascosa IP, Bai KJ. Sleep Quality among Breast and Prostate Cancer Patients: A Comparison between Subjective and Objective Measurements. Healthcare (Basel) 2021;9:785. [PMID: 34206528 DOI: 10.3390/healthcare9070785] [Reference Citation Analysis]
39 Zhu W, Xie L, Han J, Guo X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers (Basel). 2020;12:603. [PMID: 32150991 DOI: 10.3390/cancers12030603] [Cited by in Crossref: 44] [Cited by in F6Publishing: 26] [Article Influence: 22.0] [Reference Citation Analysis]
40 Park A, Nam S. Deep learning for stage prediction in neuroblastoma using gene expression data. Genomics Inform 2019;17:e30. [PMID: 31610626 DOI: 10.5808/GI.2019.17.3.e30] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
41 Duggento A, Conti A, Mauriello A, Guerrisi M, Toschi N. Deep computational pathology in breast cancer. Semin Cancer Biol 2021;72:226-37. [PMID: 32818626 DOI: 10.1016/j.semcancer.2020.08.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
42 Qiu WR, Chen G, Wu J, Lei J, Xu L, Zhang SH. Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence. Comput Math Methods Med 2021;2021:6652288. [PMID: 33505514 DOI: 10.1155/2021/6652288] [Reference Citation Analysis]
43 Hatzidaki E, Iliopoulos A, Papasotiriou I. A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning. Entropy (Basel) 2021;23:1248. [PMID: 34681972 DOI: 10.3390/e23101248] [Reference Citation Analysis]
44 Huang J, Xie X, Wu H, Zhang X, Zheng Y, Xie X, Wang Y, Xu M. Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms. Eur Radiol 2022. [PMID: 35389050 DOI: 10.1007/s00330-022-08703-9] [Reference Citation Analysis]