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For: Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019;11:E1673. [PMID: 31661863 DOI: 10.3390/cancers11111673] [Cited by in Crossref: 33] [Cited by in F6Publishing: 30] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
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2 Ali M, Ali R. Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification. Diagnostics (Basel) 2021;11:1485. [PMID: 34441419 DOI: 10.3390/diagnostics11081485] [Reference Citation Analysis]
3 Wang X, Li BB. Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet 2021;12:624820. [PMID: 33643386 DOI: 10.3389/fgene.2021.624820] [Reference Citation Analysis]
4 Blaivas L, Blaivas M. Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose-Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X-Ray, and Point-of-Care Ultrasound Images for Quality. J Ultrasound Med 2021;40:377-83. [PMID: 32757235 DOI: 10.1002/jum.15413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
5 Kumar N, Sharma M, Singh VP, Madan C, Mehandia S. An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images. Biomedical Signal Processing and Control 2022;75:103596. [DOI: 10.1016/j.bspc.2022.103596] [Reference Citation Analysis]
6 Malherbe K. Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis. Am J Pathol 2021;191:1364-73. [PMID: 33639101 DOI: 10.1016/j.ajpath.2021.01.014] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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8 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]
9 Kazdal D, Hofman V, Christopoulos P, Ilié M, Stenzinger A, Hofman P. Fusion-positive non-small cell lung carcinoma: Biological principles, clinical practice and diagnostic implications. Genes Chromosomes Cancer 2022. [PMID: 34997651 DOI: 10.1002/gcc.23022] [Reference Citation Analysis]
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11 Svahn TM, Sjöberg T, Shahgeldi K, Zacharias F, Ast JC, Parenmark M. COMPARISON OF PULMONARY NODULE DETECTION, READING TIMES AND PATIENT DOSES OF ULTRA-LOW DOSE CT, STANDARD DOSE CT AND DIGITAL RADIOGRAPHY. Radiat Prot Dosimetry 2021;196:234-40. [PMID: 34693453 DOI: 10.1093/rpd/ncab154] [Reference Citation Analysis]
12 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]
13 Wei P. Radiomics, deep learning and early diagnosis in oncology. Emerg Top Life Sci 2021;5:829-35. [PMID: 34874454 DOI: 10.1042/ETLS20210218] [Reference Citation Analysis]
14 Chen SH, Xu LY, Wu YP, Ke ZB, Huang P, Lin F, Li XD, Xue XY, Wei Y, Zheng QS, Xu N. Tumor volume: a new prognostic factor of oncological outcome of localized clear cell renal cell carcinoma. BMC Cancer 2021;21:79. [PMID: 33468079 DOI: 10.1186/s12885-021-07795-8] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Park S, Wang X, Lim J, Xiao G, Lu T, Wang T. Bayesian multiple instance regression for modeling immunogenic neoantigens. Stat Methods Med Res 2020;29:3032-47. [PMID: 32401701 DOI: 10.1177/0962280220914321] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Paladini E, Vantaggiato E, Bougourzi F, Distante C, Hadid A, Taleb-Ahmed A. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J Imaging 2021;7:51. [PMID: 34460707 DOI: 10.3390/jimaging7030051] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
17 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]
18 Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2021;:21925682211035363. [PMID: 34628966 DOI: 10.1177/21925682211035363] [Reference Citation Analysis]
19 Tao Y, Huang X, Tan Y, Wang H, Jiang W, Chen Y, Wang C, Luo J, Liu Z, Gao K, Yang W, Guo M, Tang B, Zhou A, Yao M, Chen T, Cao Y, Luo C, Zhang J. Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study. Front Oncol 2021;11:735739. [PMID: 34692509 DOI: 10.3389/fonc.2021.735739] [Reference Citation Analysis]
20 Elkhader J, Elemento O. Artificial intelligence in oncology: From bench to clinic. Semin Cancer Biol 2021:S1044-579X(21)00114-0. [PMID: 33915289 DOI: 10.1016/j.semcancer.2021.04.013] [Reference Citation Analysis]
21 DiPalma J, Suriawinata AA, Tafe LJ, Torresani L, Hassanpour S. Resolution-based distillation for efficient histology image classification. Artif Intell Med 2021;119:102136. [PMID: 34531005 DOI: 10.1016/j.artmed.2021.102136] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 Mahmood H, Shaban M, Indave BI, Santos-Silva AR, Rajpoot N, Khurram SA. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol 2020;110:104885. [PMID: 32674040 DOI: 10.1016/j.oraloncology.2020.104885] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
23 Guan Y, Ren M, Guo D, He Y. [Research Progress on Lung Cancer Screening]. Zhongguo Fei Ai Za Zhi 2020;23:954-60. [PMID: 32819054 DOI: 10.3779/j.issn.1009-3419.2020.101.37] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Del Re M, Cucchiara F, Petrini I, Fogli S, Passaro A, Crucitta S, Attili I, De Marinis F, Chella A, Danesi R. erbB in NSCLC as a molecular target: current evidences and future directions. ESMO Open 2020;5:e000724. [PMID: 32820012 DOI: 10.1136/esmoopen-2020-000724] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
25 Lee ALS, To CCK, Lee ALH, Li JJX, Chan RCK. Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images. Informatics in Medicine Unlocked 2022;28:100850. [DOI: 10.1016/j.imu.2022.100850] [Reference Citation Analysis]
26 Pérez E, Ventura S. Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules. Cancers (Basel) 2021;13:4974. [PMID: 34638456 DOI: 10.3390/cancers13194974] [Reference Citation Analysis]
27 Murugesan M, Kaliannan K, Balraj S, Singaram K, Kaliannan T, Albert JR. A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images. IFS 2022;42:2667-79. [DOI: 10.3233/jifs-212189] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
28 Deng S, Zhang X, Yan W, Chang EI, Fan Y, Lai M, Xu Y. Deep learning in digital pathology image analysis: a survey. Front Med 2020;14:470-87. [PMID: 32728875 DOI: 10.1007/s11684-020-0782-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
29 Zaizen Y, Kanahori Y, Ishijima S, Kitamura Y, Yoon H, Ozasa M, Mukae H, Bychkov A, Hoshino T, Fukuoka J. Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests. Diagnostics 2022;12:709. [DOI: 10.3390/diagnostics12030709] [Reference Citation Analysis]
30 Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopoulos P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers (Basel). 2020;12. [PMID: 32560475 DOI: 10.3390/cancers12061604] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
31 Cheng X, Zhang W, Wu M, Jiang N, Guo Z, Leng X, Song J, Jin H, Sun X, Zhang F, Qin J, Yan X, Cai Z, Luo Y, Yang Y, Liu J. A prediction of hematoma expansion in hemorrhagic patients using a novel dual-modal machine learning strategy. Physiol Meas 2021;42. [PMID: 34198278 DOI: 10.1088/1361-6579/ac10ab] [Reference Citation Analysis]
32 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]
33 Nishio M, Nishio M, Jimbo N, Nakane K. Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue. Cancers (Basel) 2021;13:1192. [PMID: 33801859 DOI: 10.3390/cancers13061192] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
34 Zhang L, Rong R, Li Q, Yang DM, Yao B, Luo D, Zhang X, Zhu X, Luo J, Liu Y, Yang X, Ji X, Liu Z, Xie Y, Sha Y, Li Z, Xiao G. A deep learning-based model for screening and staging pneumoconiosis. Sci Rep 2021;11:2201. [PMID: 33500426 DOI: 10.1038/s41598-020-77924-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
35 Toh J, Hoppe MM, Thakur T, Yang H, Tan KT, Pang B, Ho S, Roy R, Ho KY, Yeoh KG, Tan P, Sundar R, Jeyasekharan A. Profiling of gastric cancer cell-surface markers to achieve tumour-normal discrimination. BMJ Open Gastroenterol 2020;7:e000452. [PMID: 32816956 DOI: 10.1136/bmjgast-2020-000452] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Hofman P, Rouleau E, Sabourin JC, Denis M, Deleuze JF, Barlesi F, Laurent-Puig P. Predictive molecular pathology in non-small cell lung cancer in France: The past, the present and the perspectives. Cancer Cytopathol 2020;128:601-10. [PMID: 32885912 DOI: 10.1002/cncy.22318] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
37 Im S, Hyeon J, Rha E, Lee J, Choi HJ, Jung Y, Kim TJ. Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning. Sensors (Basel) 2021;21:3500. [PMID: 34067934 DOI: 10.3390/s21103500] [Reference Citation Analysis]
38 Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers (Basel). 2020;12. [PMID: 32668721 DOI: 10.3390/cancers12071884] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
39 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]
40 Qian H, Dong B, Yuan JJ, Yin F, Wang Z, Wang HN, Wang HS, Tian D, Li WH, Zhang B, Zhao LB, Ning BT. Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics. Front Med (Lausanne) 2021;8:695185. [PMID: 34820391 DOI: 10.3389/fmed.2021.695185] [Reference Citation Analysis]
41 Gonzalez D, Dietz RL, Pantanowitz L. Feasibility of a deep learning algorithm to distinguish large cell neuroendocrine from small cell lung carcinoma in cytology specimens. Cytopathology 2020;31:426-31. [PMID: 32246504 DOI: 10.1111/cyt.12829] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
42 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]