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Cited by in F6Publishing
For: Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R. The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning. Biomolecules. 2020;10. [PMID: 32751349 DOI: 10.3390/biom10081123] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Arif M, Philip FM, Ajesh F, Izdrui D, Craciun MD, Geman O, Chakraborty C. Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network. Journal of Healthcare Engineering 2022;2022:1-15. [DOI: 10.1155/2022/6952304] [Reference Citation Analysis]
3 Zhang J, Wang Y, Liu J, Tang Z, Wang Z. Multiple organ-specific cancers classification from PET/CT images using deep learning. Multimed Tools Appl 2022;81:16133-54. [DOI: 10.1007/s11042-022-12055-3] [Reference Citation Analysis]
4 Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021;9:1513. [PMID: 34829742 DOI: 10.3390/biomedicines9111513] [Reference Citation Analysis]
5 Liu D, Zhang W, Hu F, Yu P, Zhang X, Yin H, Yang L, Fang X, Song B, Wu B, Hu J, Huang Z. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study. Front Oncol 2021;11:777760. [PMID: 34926287 DOI: 10.3389/fonc.2021.777760] [Reference Citation Analysis]
6 Yamada M, Saito Y, Yamada S, Kondo H, Hamamoto R. Detection of flat colorectal neoplasia by artificial intelligence: A systematic review. Best Pract Res Clin Gastroenterol 2021;52-53:101745. [PMID: 34172250 DOI: 10.1016/j.bpg.2021.101745] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
7 Rahman Z, Hossain MS, Islam MR, Hasan MM, Hridhee RA. An approach for multiclass skin lesion classification based on ensemble learning. Informatics in Medicine Unlocked 2021;25:100659. [DOI: 10.1016/j.imu.2021.100659] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
8 Park S, Saw SN, Li X, Paknezhad M, Coppola D, Dinish US, Ebrahim Attia AB, Yew YW, Guan Thng ST, Lee HK, Olivo M. Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomed Opt Express 2021;12:3671-83. [PMID: 34221687 DOI: 10.1364/BOE.415105] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022;146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Reference Citation Analysis]
10 Jutzi TB, Krieghoff-henning EI, Brinker TJ. Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome. Aktuelle Dermatologie 2022;48:84-91. [DOI: 10.1055/a-1514-2013] [Reference Citation Analysis]
11 Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27(22): 2979-2993 [PMID: 34168402 DOI: 10.3748/wjg.v27.i22.2979] [Reference Citation Analysis]
12 Yang S, Shu C, Hu H, Ma G, Yang M, Hussein AF. Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/9726181] [Reference Citation Analysis]
13 Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel). 2020;12. [PMID: 33256107 DOI: 10.3390/cancers12123532] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
14 Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021;9:720. [PMID: 34201827 DOI: 10.3390/biomedicines9070720] [Reference Citation Analysis]
15 Cassidy B, Kendrick C, Brodzicki A, Jaworek-Korjakowska J, Yap MH. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Med Image Anal 2022;75:102305. [PMID: 34852988 DOI: 10.1016/j.media.2021.102305] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
16 Khan IU, Aslam N, Anwar T, Aljameel SS, Ullah M, Khan R, Rehman A, Akhtar N, Pan Y. Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting. Complexity 2021;2021:1-13. [DOI: 10.1155/2021/5591614] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Daneshjou R, Smith MP, Sun MD, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatol 2021;157:1362-9. [PMID: 34550305 DOI: 10.1001/jamadermatol.2021.3129] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Haggenmüller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, Beltraminelli H, Berking C, Betz-Stablein B, Blum A, Braun SA, Carr R, Combalia M, Fernandez-Figueras MT, Ferrara G, Fraitag S, French LE, Gellrich FF, Ghoreschi K, Goebeler M, Guitera P, Haenssle HA, Haferkamp S, Heinzerling L, Heppt MV, Hilke FJ, Hobelsberger S, Krahl D, Kutzner H, Lallas A, Liopyris K, Llamas-Velasco M, Malvehy J, Meier F, Müller CSL, Navarini AA, Navarrete-Dechent C, Perasole A, Poch G, Podlipnik S, Requena L, Rotemberg VM, Saggini A, Sangueza OP, Santonja C, Schadendorf D, Schilling B, Schlaak M, Schlager JG, Sergon M, Sondermann W, Soyer HP, Starz H, Stolz W, Vale E, Weyers W, Zink A, Krieghoff-Henning E, Kather JN, von Kalle C, Lipka DB, Fröhling S, Hauschild A, Kittler H, Brinker TJ. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer 2021;156:202-16. [PMID: 34509059 DOI: 10.1016/j.ejca.2021.06.049] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
19 Kaur R, GholamHosseini H, Sinha R, Lindén M. Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images. Sensors (Basel) 2022;22:1134. [PMID: 35161878 DOI: 10.3390/s22031134] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
20 Takahashi S, Takahashi M, Tanaka S, Takayanagi S, Takami H, Yamazawa E, Nambu S, Miyake M, Satomi K, Ichimura K, Narita Y, Hamamoto R. A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning. Biomolecules 2021;11:565. [PMID: 33921457 DOI: 10.3390/biom11040565] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Lucius M, De All J, De All JA, Belvisi M, Radizza L, Lanfranconi M, Lorenzatti V, Galmarini CM. Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions. Diagnostics (Basel) 2020;10:E969. [PMID: 33218060 DOI: 10.3390/diagnostics10110969] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
22 Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021;11:886. [PMID: 34575663 DOI: 10.3390/jpm11090886] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Hamamoto R. Application of Artificial Intelligence for Medical Research. Biomolecules 2021;11:90. [PMID: 33445802 DOI: 10.3390/biom11010090] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
24 Bechelli S, Delhommelle J. Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images. Bioengineering 2022;9:97. [DOI: 10.3390/bioengineering9030097] [Reference Citation Analysis]
25 C. SR, G J, N S, Padmaja DL, Nagaprasad S, Pant K, Kumar YP, Khan R. Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food. Journal of Food Quality 2022;2022:1-12. [DOI: 10.1155/2022/5890666] [Reference Citation Analysis]
26 Takahashi S, Takahashi M, Kinoshita M, Miyake M, Kawaguchi R, Shinojima N, Mukasa A, Saito K, Nagane M, Otani R, Higuchi F, Tanaka S, Hata N, Tamura K, Tateishi K, Nishikawa R, Arita H, Nonaka M, Uda T, Fukai J, Okita Y, Tsuyuguchi N, Kanemura Y, Kobayashi K, Sese J, Ichimura K, Narita Y, Hamamoto R. Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities. Cancers (Basel) 2021;13:1415. [PMID: 33808802 DOI: 10.3390/cancers13061415] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
27 Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, Shimoyama R, Komatsu M, Hamamoto R. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Front Oncol 2021;11:666937. [PMID: 34055633 DOI: 10.3389/fonc.2021.666937] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
28 Thapar P, Rakhra M, Cazzato G, Hossain MS, Garg D. A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification. Journal of Healthcare Engineering 2022;2022:1-21. [DOI: 10.1155/2022/1709842] [Reference Citation Analysis]