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For: Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 2018;24:1559-67. [PMID: 30224757 DOI: 10.1038/s41591-018-0177-5] [Cited by in Crossref: 668] [Cited by in F6Publishing: 509] [Article Influence: 167.0] [Reference Citation Analysis]
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4 Xie CY, Pang CL, Chan B, Wong EY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021;13:2469. [PMID: 34069367 DOI: 10.3390/cancers13102469] [Reference Citation Analysis]
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8 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]
9 Shao X, Zhang H, Wang Y, Qian H, Zhu Y, Dong B, Xu F, Chen N, Liu S, Pan J, Xue W. Deep convolutional neural networks combine Raman spectral signature of serum for prostate cancer bone metastases screening. Nanomedicine 2020;29:102245. [PMID: 32592757 DOI: 10.1016/j.nano.2020.102245] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
10 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]
11 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]
12 Romero D. AI to assess images. Nat Rev Clin Oncol 2018;15:724. [PMID: 30266916 DOI: 10.1038/s41571-018-0107-y] [Reference Citation Analysis]
13 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]
14 Clymer D, Kostadinov S, Catov J, Skvarca L, Pantanowitz L, Cagan J, LeDuc P. Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks. Am J Pathol 2020;190:2111-22. [PMID: 32679230 DOI: 10.1016/j.ajpath.2020.06.014] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 Zhao S, Liu Q, Li J, Hu C, Cao F, Ma W, Gao J. Construction and Validation of Prognostic Regulation Network Based on RNA-Binding Protein Genes in Lung Squamous Cell Carcinoma. DNA Cell Biol 2021;40:1563-83. [PMID: 34931870 DOI: 10.1089/dna.2021.0145] [Reference Citation Analysis]
16 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]
17 [DOI: 10.1101/554527] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
18 Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep 2019;9:3358. [PMID: 30833650 DOI: 10.1038/s41598-019-40041-7] [Cited by in Crossref: 53] [Cited by in F6Publishing: 52] [Article Influence: 17.7] [Reference Citation Analysis]
19 Chen L, Zeng H, Xiang Y, Huang Y, Luo Y, Ma X. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Front Cell Dev Biol 2021;9:720110. [PMID: 34708036 DOI: 10.3389/fcell.2021.720110] [Reference Citation Analysis]
20 Öztürk Ş, Akdemir B. HIC-net: A deep convolutional neural network model for classification of histopathological breast images. Computers & Electrical Engineering 2019;76:299-310. [DOI: 10.1016/j.compeleceng.2019.04.012] [Cited by in Crossref: 20] [Cited by in F6Publishing: 2] [Article Influence: 6.7] [Reference Citation Analysis]
21 Gong J, Liu J, Li H, Zhu H, Wang T, Hu T, Li M, Xia X, Hu X, Peng W, Wang S, Tong T, Gu Y. Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study. Cancers (Basel) 2021;13:3300. [PMID: 34209366 DOI: 10.3390/cancers13133300] [Reference Citation Analysis]
22 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]
23 Zhou Y, Cao Y, Huang J, Deng K, Ma K, Zhang T, Chen L, Zhang J, Huang P. Research advances in forensic diatom testing. Forensic Sci Res 2020;5:98-105. [PMID: 32939425 DOI: 10.1080/20961790.2020.1718901] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
24 Kan G, Wang Z, Sheng C, Yao C, Mao Y, Chen S. Inhibition of DKC1 induces telomere-related senescence and apoptosis in lung adenocarcinoma. J Transl Med 2021;19:161. [PMID: 33879171 DOI: 10.1186/s12967-021-02827-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 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]
26 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]
27 Wu K, Wu P, Yang K, Li Z, Kong S, Yu L, Zhang E, Liu H, Guo Q, Wu S. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images. Eur Radiol 2021. [PMID: 34800150 DOI: 10.1007/s00330-021-08353-3] [Reference Citation Analysis]
28 [DOI: 10.1101/2020.03.12.20027185] [Cited by in Crossref: 158] [Cited by in F6Publishing: 11] [Reference Citation Analysis]
29 Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, Kaku M, Zhou Y, Alderazi YJ, Swaminathan A, Kedar S, Saint-Hilaire MH, Auerbach SH, Yuan J, Sartor EA, Au R, Kolachalama VB. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain 2020;143:1920-33. [PMID: 32357201 DOI: 10.1093/brain/awaa137] [Cited by in Crossref: 28] [Cited by in F6Publishing: 18] [Article Influence: 28.0] [Reference Citation Analysis]
30 Klimov S, Xue Y, Gertych A, Graham RP, Jiang Y, Bhattarai S, Pandol SJ, Rakha EA, Reid MD, Aneja R. Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis. Front Oncol 2020;10:593211. [PMID: 33718106 DOI: 10.3389/fonc.2020.593211] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
31 Zhang C, Zhang M, Ge S, Huang W, Lin X, Gao J, Gong J, Shen L. Reduced m6A modification predicts malignant phenotypes and augmented Wnt/PI3K-Akt signaling in gastric cancer. Cancer Med 2019;8:4766-81. [PMID: 31243897 DOI: 10.1002/cam4.2360] [Cited by in Crossref: 72] [Cited by in F6Publishing: 78] [Article Influence: 24.0] [Reference Citation Analysis]
32 Alrassi J, Katsufrakis PJ, Chandran L. Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care. Acad Med 2021;96:37-43. [PMID: 32910005 DOI: 10.1097/ACM.0000000000003733] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
33 Sabourin JC. [I have a dream: Pathology Laboratory of the future!]. Ann Pathol 2019;39:77-9. [PMID: 30853498 DOI: 10.1016/j.annpat.2019.02.004] [Reference Citation Analysis]
34 Roohi A, Faust K, Djuric U, Diamandis P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surg Pathol Clin 2020;13:349-58. [PMID: 32389272 DOI: 10.1016/j.path.2020.01.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
35 Letterie G. Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. J Assist Reprod Genet 2021;38:1617-25. [PMID: 33870475 DOI: 10.1007/s10815-021-02159-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Wan JJ, Chen BL, Kong YX, Ma XG, Yu YT. An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network. Sci Rep 2019;9:17418. [PMID: 31758076 DOI: 10.1038/s41598-019-54031-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
37 Zhou Y, Zhang J, Huang J, Deng K, Zhang J, Qin Z, Wang Z, Zhang X, Tuo Y, Chen L, Chen Y, Huang P. Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm. Forensic Science International 2019;302:109922. [DOI: 10.1016/j.forsciint.2019.109922] [Cited by in Crossref: 16] [Cited by in F6Publishing: 8] [Article Influence: 5.3] [Reference Citation Analysis]
38 Faust K, Bala S, van Ommeren R, Portante A, Al Qawahmed R, Djuric U, Diamandis P. Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning. Nat Mach Intell 2019;1:316-21. [DOI: 10.1038/s42256-019-0068-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
39 Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet JM, Becheur H, Fein F, Cojocarasu O, Kaminsky MC, Lagasse JP, Luet D, Nguyen S, Etienne PL, Gasmi M, Vanoli A, Perrier H, Puig PL, Emile JF, Lepage C, Ghiringhelli F. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut 2020;69:681-90. [PMID: 31780575 DOI: 10.1136/gutjnl-2019-319292] [Cited by in Crossref: 22] [Cited by in F6Publishing: 29] [Article Influence: 7.3] [Reference Citation Analysis]
40 Liao H, Long Y, Han R, Wang W, Xu L, Liao M, Zhang Z, Wu Z, Shang X, Li X, Peng J, Yuan K, Zeng Y. Deep learning-based classification and mutation prediction from histopathological images of hepatocellular carcinoma. Clin Transl Med 2020;10:e102. [PMID: 32536036 DOI: 10.1002/ctm2.102] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
41 Yin L, Wang Y, Ma G, Deng Y, Zhou Q. Villi development core-related gene expression associated with lung squamous cancer prognosis. Medicine (Baltimore) 2019;98:e14714. [PMID: 30882635 DOI: 10.1097/MD.0000000000014714] [Reference Citation Analysis]
42 Coccia M. Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technology in Society 2020;60:101198. [DOI: 10.1016/j.techsoc.2019.101198] [Cited by in Crossref: 38] [Cited by in F6Publishing: 9] [Article Influence: 19.0] [Reference Citation Analysis]
43 Xu W, Lin J, Gao M, Chen Y, Cao J, Pu J, Huang L, Zhao J, Qian K. Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition. Adv Sci (Weinh) 2020;7:2002021. [PMID: 33173737 DOI: 10.1002/advs.202002021] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 8.5] [Reference Citation Analysis]
44 Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D'Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med 2020;26:52-8. [PMID: 31907460 DOI: 10.1038/s41591-019-0715-9] [Cited by in Crossref: 110] [Cited by in F6Publishing: 95] [Article Influence: 55.0] [Reference Citation Analysis]
45 You S, Sun Y, Yang L, Park J, Tu H, Marjanovic M, Sinha S, Boppart SA. Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology. NPJ Precis Oncol 2019;3:33. [PMID: 31872065 DOI: 10.1038/s41698-019-0104-3] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
46 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]
47 Cong L, Feng W, Yao Z, Zhou X, Xiao W. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer. J Cancer 2020;11:3615-22. [PMID: 32284758 DOI: 10.7150/jca.43268] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
48 Yang Y, Yang J, Liang Y, Liao B, Zhu W, Mo X, Huang K. Identification and Validation of Efficacy of Immunological Therapy for Lung Cancer From Histopathological Images Based on Deep Learning. Front Genet 2021;12:642981. [PMID: 33633793 DOI: 10.3389/fgene.2021.642981] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 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]
50 Song Z, Yu C, Zou S, Wang W, Huang Y, Ding X, Liu J, Shao L, Yuan J, Gou X, Jin W, Wang Z, Chen X, Chen H, Liu C, Xu G, Sun Z, Ku C, Zhang Y, Dong X, Wang S, Xu W, Lv N, Shi H. Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists. BMJ Open. 2020;10:e036423. [PMID: 32912980 DOI: 10.1136/bmjopen-2019-036423] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
51 Kubach J, Muhlebner-Fahrngruber A, Soylemezoglu F, Miyata H, Niehusmann P, Honavar M, Rogerio F, Kim SH, Aronica E, Garbelli R, Vilz S, Popp A, Walcher S, Neuner C, Scholz M, Kuerten S, Schropp V, Roeder S, Eichhorn P, Eckstein M, Brehmer A, Kobow K, Coras R, Blumcke I, Jabari S. Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations. Epilepsia 2020;61:421-32. [PMID: 32080846 DOI: 10.1111/epi.16447] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
52 Wang X, Chen P, Ding G, Xing Y, Tang R, Peng C, Ye Y, Fu Q. Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer. Medicine (Baltimore) 2021;100:e25994. [PMID: 34011092 DOI: 10.1097/MD.0000000000025994] [Reference Citation Analysis]
53 Huang J, Wang D, Da J. Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis. Diagn Cytopathol 2020;48:1027-33. [PMID: 32530573 DOI: 10.1002/dc.24520] [Reference Citation Analysis]
54 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
55 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]
56 Daneshjou R, He B, Ouyang D, Zou JY. How to evaluate deep learning for cancer diagnostics - factors and recommendations. Biochim Biophys Acta Rev Cancer 2021;1875:188515. [PMID: 33513392 DOI: 10.1016/j.bbcan.2021.188515] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
57 Hewitt LC, Saito Y, Wang T, Matsuda Y, Oosting J, Silva ANS, Slaney HL, Melotte V, Hutchins G, Tan P, Yoshikawa T, Arai T, Grabsch HI. KRAS status is related to histological phenotype in gastric cancer: results from a large multicentre study. Gastric Cancer 2019;22:1193-203. [PMID: 31111275 DOI: 10.1007/s10120-019-00972-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
58 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]
59 Cortés-Ciriano I, Gulhan DC, Lee JJ, Melloni GEM, Park PJ. Computational analysis of cancer genome sequencing data. Nat Rev Genet 2021. [PMID: 34880424 DOI: 10.1038/s41576-021-00431-y] [Reference Citation Analysis]
60 Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, Gelb BD, Ginsburg GS, Hassenstab J, Ho CM, Mobley WC, Nolan GP, Rosen ST, Tan P, Yen Y, Zarrinpar A. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol 2020;38:497-518. [PMID: 31980301 DOI: 10.1016/j.tibtech.2019.12.021] [Cited by in Crossref: 40] [Cited by in F6Publishing: 32] [Article Influence: 20.0] [Reference Citation Analysis]
61 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]
62 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]
63 Sakellaropoulos T, Vougas K, Narang S, Koinis F, Kotsinas A, Polyzos A, Moss TJ, Piha-Paul S, Zhou H, Kardala E, Damianidou E, Alexopoulos LG, Aifantis I, Townsend PA, Panayiotidis MI, Sfikakis P, Bartek J, Fitzgerald RC, Thanos D, Mills Shaw KR, Petty R, Tsirigos A, Gorgoulis VG. A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Rep. 2019;29:3367-3373.e4. [PMID: 31825821 DOI: 10.1016/j.celrep.2019.11.017] [Cited by in Crossref: 41] [Cited by in F6Publishing: 32] [Article Influence: 20.5] [Reference Citation Analysis]
64 Dutta R, Khalil R, Green R, Mohapatra SS, Mohapatra S. Withania Somnifera (Ashwagandha) and Withaferin A: Potential in Integrative Oncology. Int J Mol Sci. 2019;20. [PMID: 31731424 DOI: 10.3390/ijms20215310] [Cited by in Crossref: 25] [Cited by in F6Publishing: 15] [Article Influence: 8.3] [Reference Citation Analysis]
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