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For: Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, Bohr C, Neumann H, Stelzle F, Maier A. Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning. Sci Rep 2017;7:11979. [PMID: 28931888 DOI: 10.1038/s41598-017-12320-8] [Cited by in Crossref: 85] [Cited by in F6Publishing: 53] [Article Influence: 17.0] [Reference Citation Analysis]
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15 Manhas J, Gupta RK, Roy PP. A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities. Arch Computat Methods Eng. [DOI: 10.1007/s11831-021-09676-6] [Reference Citation Analysis]
16 Uthoff RD, Song B, Sunny S, Patrick S, Suresh A, Kolur T, Keerthi G, Spires O, Anbarani A, Wilder-Smith P, Kuriakose MA, Birur P, Liang R. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PLoS One 2018;13:e0207493. [PMID: 30517120 DOI: 10.1371/journal.pone.0207493] [Cited by in Crossref: 34] [Cited by in F6Publishing: 26] [Article Influence: 8.5] [Reference Citation Analysis]
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23 Sievert M, Mantsopoulos K, Mueller SK, Rupp R, Eckstein M, Stelzle F, Oetter N, Maier A, Aubreville M, Iro H, Goncalves M. Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy. Braz J Otorhinolaryngol 2021:S1808-8694(21)00124-5. [PMID: 34348858 DOI: 10.1016/j.bjorl.2021.06.002] [Reference Citation Analysis]
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25 Song B, Sunny S, Uthoff RD, Patrick S, Suresh A, Kolur T, Keerthi G, Anbarani A, Wilder-Smith P, Kuriakose MA, Birur P, Rodriguez JJ, Liang R. Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning. Biomed Opt Express 2018;9:5318-29. [PMID: 30460130 DOI: 10.1364/BOE.9.005318] [Cited by in Crossref: 23] [Cited by in F6Publishing: 13] [Article Influence: 5.8] [Reference Citation Analysis]
26 Nam S, Kim D, Jung W, Zhu Y. Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis. J Med Internet Res 2022;24:e28114. [PMID: 35451980 DOI: 10.2196/28114] [Reference Citation Analysis]
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28 Yang X, Liu W. Current evidence on confocal laser endomicroscopy for noninvasive head and neck cancer imaging. Acta Otorhinolaryngol Ital 2020;40:396-8. [PMID: 33299231 DOI: 10.14639/0392-100X-N0801] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Aubreville M, Goncalves M, Knipfer C, Oetter N, Würfl T, Neumann H, Stelzle F, Bohr C, Maier A. Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract. In: Cliquet A, Wiebe S, Anderson P, Saggio G, Zwiggelaar R, Gamboa H, Fred A, Bermúdez i Badia S, editors. Biomedical Engineering Systems and Technologies. Cham: Springer International Publishing; 2019. pp. 67-85. [DOI: 10.1007/978-3-030-29196-9_4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
30 Shamim MZM, Syed S, Shiblee M, Usman M, Ali SJ, Hussein HS, Farrag M. Automated Detection of Oral Pre-Cancerous Tongue Lesions Using Deep Learning for Early Diagnosis of Oral Cavity Cancer. The Computer Journal 2020. [DOI: 10.1093/comjnl/bxaa136] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
31 Ignat M, Lindner V, Vix M, Marescaux J, Mutter D. Intraoperative Probe-Based Confocal Endomicroscopy to Histologically Differentiate Thyroid From Parathyroid Tissue Before Resection. Surg Innov 2019;26:141-8. [PMID: 30466375 DOI: 10.1177/1553350618814078] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
32 Alam IS, Steinberg I, Vermesh O, van den Berg NS, Rosenthal EL, van Dam GM, Ntziachristos V, Gambhir SS, Hernot S, Rogalla S. Emerging Intraoperative Imaging Modalities to Improve Surgical Precision. Mol Imaging Biol 2018;20:705-15. [DOI: 10.1007/s11307-018-1227-6] [Cited by in Crossref: 29] [Cited by in F6Publishing: 27] [Article Influence: 7.3] [Reference Citation Analysis]
33 Sievert M, Stelzle F, Aubreville M, Mueller SK, Eckstein M, Oetter N, Maier A, Mantsopoulos K, Iro H, Goncalves M. Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study. Eur Arch Otorhinolaryngol 2021. [PMID: 33582849 DOI: 10.1007/s00405-021-06659-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Al Kheraif AA, Alshahrani OA, Al Esawy MSS, Fouad H. Evolutionary and Ruzzo–Tompa optimized regulatory feedback neural network based evaluating tooth decay and acid erosion from 5 years old children. Measurement 2019;141:345-55. [DOI: 10.1016/j.measurement.2019.04.038] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
35 Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2(6): 104-114 [DOI: 10.35711/aimi.v2.i6.104] [Reference Citation Analysis]
36 Ziebart A, Stadniczuk D, Roos V, Ratliff M, von Deimling A, Hänggi D, Enders F. Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy. Front Oncol 2021;11:668273. [PMID: 34046358 DOI: 10.3389/fonc.2021.668273] [Reference Citation Analysis]
37 Matthies L, Gebrekidan MT, Tegtmeyer JF, Oetter N, Rohde M, Vollkommer T, Smeets R, Wilczak W, Stelzle F, Gosau M, Braeuer AS, Knipfer C. Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy. Biomed Opt Express 2021;12:836-51. [PMID: 33680545 DOI: 10.1364/BOE.409456] [Reference Citation Analysis]
38 Gessert N, Bengs M, Wittig L, Drömann D, Keck T, Schlaefer A, Ellebrecht DB. Deep transfer learning methods for colon cancer classification in confocal laser microscopy images. Int J Comput Assist Radiol Surg 2019;14:1837-45. [PMID: 31129859 DOI: 10.1007/s11548-019-02004-1] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
39 Chen S, Stromer D, Alabdalrahim HA, Schwab S, Weih M, Maier A. Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Sci Rep 2020;10:20854. [PMID: 33257744 DOI: 10.1038/s41598-020-74710-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
40 García-Pola M, Pons-Fuster E, Suárez-Fernández C, Seoane-Romero J, Romero-Méndez A, López-Jornet P. Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review. Cancers (Basel) 2021;13:4600. [PMID: 34572831 DOI: 10.3390/cancers13184600] [Reference Citation Analysis]
41 Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021;115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
42 Fatima, Imran M, Ullah A, Arif M, Noor R. A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network. Computers in Biology and Medicine 2022;145:105424. [DOI: 10.1016/j.compbiomed.2022.105424] [Reference Citation Analysis]
43 Song B, Sunny S, Li S, Gurushanth K, Mendonca P, Mukhia N, Patrick S, Gurudath S, Raghavan S, Imchen T, Leivon S, Kolur T, Shetty V, Bushan V, Ramesh R, Lima N, Pillai V, Wilder-Smith P, Sigamani A, Suresh A, Kuriakose M, Birur P, Liang R. Mobile-based oral cancer classification for point-of-care screening. J Biomed Opt 2021;26. [PMID: 34164967 DOI: 10.1117/1.JBO.26.6.065003] [Reference Citation Analysis]
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45 Belykh E, Miller EJ, Patel AA, Yazdanabadi MI, Martirosyan NL, Yağmurlu K, Bozkurt B, Byvaltsev VA, Eschbacher JM, Nakaji P, Preul MC. Diagnostic Accuracy of a Confocal Laser Endomicroscope for In Vivo Differentiation Between Normal Injured And Tumor Tissue During Fluorescein-Guided Glioma Resection: Laboratory Investigation. World Neurosurg 2018;115:e337-48. [PMID: 29673821 DOI: 10.1016/j.wneu.2018.04.048] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 3.5] [Reference Citation Analysis]
46 Mazur M, Ndokaj A, Venugopal DC, Roberto M, Albu C, Jedliński M, Tomao S, Vozza I, Trybek G, Ottolenghi L, Guerra F. In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders-Systematic Review and Meta-Analysis. Int J Environ Res Public Health 2021;18:11775. [PMID: 34831531 DOI: 10.3390/ijerph182211775] [Reference Citation Analysis]
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48 Bakkouri I, Afdel K. Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl 2020;79:20483-518. [DOI: 10.1007/s11042-019-07988-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Sharma S, Kumar S. The Xception model: A potential feature extractor in breast cancer histology images classification. ICT Express 2022;8:101-8. [DOI: 10.1016/j.icte.2021.11.010] [Reference Citation Analysis]
50 Agarwal P, Yadav A, Mathur P, Pal V, Chakrabarty A, Gupta SK. BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning. Computational Intelligence and Neuroscience 2022;2022:1-11. [DOI: 10.1155/2022/4357088] [Reference Citation Analysis]
51 Mei HX, Cheng JH, Li YZ, Ma HS, Zhang KW, Shou YK, Li Y. [Advances in the application of machine learning in maxillofacial cysts and tumors]. Hua Xi Kou Qiang Yi Xue Za Zhi 2020;38:687-91. [PMID: 33377348 DOI: 10.7518/hxkq.2020.06.014] [Reference Citation Analysis]
52 Yoshida K. Future Prospective of Light-Based Detection System for Oral Cancer and Oral Potentially Malignant Disorders by Artificial Intelligence Using Convolutional Neural Networks. Photobiomodulation, Photomedicine, and Laser Surgery 2019;37:195-6. [DOI: 10.1089/photob.2019.4621] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
53 Guo J, Wang H, Xue X, Li M, Ma Z. Real‐time classification on oral ulcer images with residual network and image enhancement. IET Image Processing 2022;16:641-6. [DOI: 10.1049/ipr2.12144] [Reference Citation Analysis]
54 Bispo MS, Pierre Júnior MLGQ, Apolinário AL Jr, Dos Santos JN, Junior BC, Neves FS, Crusoé-Rebello I. Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network. Dentomaxillofac Radiol 2021;:20210002. [PMID: 33882255 DOI: 10.1259/dmfr.20210002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
55 Stoeve M, Aubreville M, Oetter N, Knipfer C, Neumann H, Stelzle F, Maier A. Motion Artifact Detection in Confocal Laser Endomicroscopy Images. In: Maier A, Deserno TM, Handels H, Maier-hein KH, Palm C, Tolxdorff T, editors. Bildverarbeitung für die Medizin 2018. Berlin: Springer Berlin Heidelberg; 2018. pp. 328-33. [DOI: 10.1007/978-3-662-56537-7_85] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
56 Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. Front Oral Health 2021;2:686863. [PMID: 35048032 DOI: 10.3389/froh.2021.686863] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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59 Cowger W, Gray A, Christiansen SH, DeFrond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, Munno K, Ossmann BE, Pittroff M, Rochman C, Sarau G, Tarby S, Primpke S. Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research. Appl Spectrosc 2020;74:989-1010. [PMID: 32500727 DOI: 10.1177/0003702820929064] [Cited by in Crossref: 25] [Cited by in F6Publishing: 21] [Article Influence: 12.5] [Reference Citation Analysis]
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66 Song B, Sunny S, Li S, Gurushanth K, Mendonca P, Mukhia N, Patrick S, Gurudath S, Raghavan S, Tsusennaro I, Leivon ST, Kolur T, Shetty V, Bushan VR, Ramesh R, Peterson T, Pillai V, Wilder-Smith P, Sigamani A, Suresh A, Kuriakose MA, Birur P, Liang R. Bayesian deep learning for reliable oral cancer image classification. Biomed Opt Express 2021;12:6422-30. [PMID: 34745746 DOI: 10.1364/BOE.432365] [Reference Citation Analysis]
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