189 related articles for article (PubMed ID: 35569282)
21. Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.
Kommoss KS; Winkler JK; Mueller-Christmann C; Bardehle F; Toberer F; Stolz W; Kraenke T; Hofmann-Wellenhof R; Blum A; Enk A; Rosenberger A; Haenssle HA
Eur J Cancer; 2023 May; 185():53-60. PubMed ID: 36963352
[TBL] [Abstract][Full Text] [Related]
22. Dermatologist-level classification of skin cancer with deep neural networks.
Esteva A; Kuprel B; Novoa RA; Ko J; Swetter SM; Blau HM; Thrun S
Nature; 2017 Feb; 542(7639):115-118. PubMed ID: 28117445
[TBL] [Abstract][Full Text] [Related]
23. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.
Goessinger EV; Cerminara SE; Mueller AM; Gottfrois P; Huber S; Amaral M; Wenz F; Kostner L; Weiss L; Kunz M; Maul JT; Wespi S; Broman E; Kaufmann S; Patpanathapillai V; Treyer I; Navarini AA; Maul LV
J Eur Acad Dermatol Venereol; 2024 May; 38(5):945-953. PubMed ID: 38158385
[TBL] [Abstract][Full Text] [Related]
24. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.
Fujisawa Y; Otomo Y; Ogata Y; Nakamura Y; Fujita R; Ishitsuka Y; Watanabe R; Okiyama N; Ohara K; Fujimoto M
Br J Dermatol; 2019 Feb; 180(2):373-381. PubMed ID: 29953582
[TBL] [Abstract][Full Text] [Related]
25. Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.
Gillstedt M; Mannius L; Paoli J; Dahlén Gyllencreutz J; Fougelberg J; Johansson Backman E; Pakka J; Zaar O; Polesie S
Acta Derm Venereol; 2022 Oct; 102():adv00790. PubMed ID: 36172695
[TBL] [Abstract][Full Text] [Related]
26. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
Tschandl P; Rosendahl C; Akay BN; Argenziano G; Blum A; Braun RP; Cabo H; Gourhant JY; Kreusch J; Lallas A; Lapins J; Marghoob A; Menzies S; Neuber NM; Paoli J; Rabinovitz HS; Rinner C; Scope A; Soyer HP; Sinz C; Thomas L; Zalaudek I; Kittler H
JAMA Dermatol; 2019 Jan; 155(1):58-65. PubMed ID: 30484822
[TBL] [Abstract][Full Text] [Related]
27. Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.
Alshahrani M; Al-Jabbar M; Senan EM; Ahmed IA; Mohammed Saif JA
PLoS One; 2024; 19(3):e0298305. PubMed ID: 38512890
[TBL] [Abstract][Full Text] [Related]
28. Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.
Sies K; Winkler JK; Fink C; Bardehle F; Toberer F; Buhl T; Enk A; Blum A; Rosenberger A; Haenssle HA
Eur J Cancer; 2020 Aug; 135():39-46. PubMed ID: 32534243
[TBL] [Abstract][Full Text] [Related]
29. Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.
Brinker TJ; Hekler A; Enk AH; von Kalle C
PLoS One; 2019; 14(6):e0218713. PubMed ID: 31233565
[TBL] [Abstract][Full Text] [Related]
30. Collective human intelligence outperforms artificial intelligence in a skin lesion classification task.
Winkler JK; Sies K; Fink C; Toberer F; Enk A; Abassi MS; Fuchs T; Blum A; Stolz W; Coras-Stepanek B; Cipic R; Guther S; Haenssle HA
J Dtsch Dermatol Ges; 2021 Aug; 19(8):1178-1184. PubMed ID: 34096688
[TBL] [Abstract][Full Text] [Related]
31. Discrimination Between Invasive and
Polesie S; Gillstedt M; Ahlgren G; Ceder H; Dahlén Gyllencreutz J; Fougelberg J; Johansson Backman E; Pakka J; Zaar O; Paoli J
Front Med (Lausanne); 2021; 8():723914. PubMed ID: 34595193
[No Abstract] [Full Text] [Related]
32. Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions.
Foahom Gouabou AC; Collenne J; Monnier J; Iguernaissi R; Damoiseaux JL; Moudafi A; Merad D
Int J Mol Sci; 2022 Nov; 23(22):. PubMed ID: 36430315
[TBL] [Abstract][Full Text] [Related]
33. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
Winkler JK; Fink C; Toberer F; Enk A; Deinlein T; Hofmann-Wellenhof R; Thomas L; Lallas A; Blum A; Stolz W; Haenssle HA
JAMA Dermatol; 2019 Oct; 155(10):1135-1141. PubMed ID: 31411641
[TBL] [Abstract][Full Text] [Related]
34. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.
Marchetti MA; Codella NCF; Dusza SW; Gutman DA; Helba B; Kalloo A; Mishra N; Carrera C; Celebi ME; DeFazio JL; Jaimes N; Marghoob AA; Quigley E; Scope A; Yélamos O; Halpern AC;
J Am Acad Dermatol; 2018 Feb; 78(2):270-277.e1. PubMed ID: 28969863
[TBL] [Abstract][Full Text] [Related]
35. Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.
Höhn J; Hekler A; Krieghoff-Henning E; Kather JN; Utikal JS; Meier F; Gellrich FF; Hauschild A; French L; Schlager JG; Ghoreschi K; Wilhelm T; Kutzner H; Heppt M; Haferkamp S; Sondermann W; Schadendorf D; Schilling B; Maron RC; Schmitt M; Jutzi T; Fröhling S; Lipka DB; Brinker TJ
J Med Internet Res; 2021 Jul; 23(7):e20708. PubMed ID: 34255646
[TBL] [Abstract][Full Text] [Related]
36. Does sex matter? Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection.
Sies K; Winkler JK; Fink C; Bardehle F; Toberer F; Buhl T; Enk A; Blum A; Stolz W; Rosenberger A; Haenssle HA
Eur J Cancer; 2022 Mar; 164():88-94. PubMed ID: 35182926
[TBL] [Abstract][Full Text] [Related]
37. Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study.
Han SS; Moon IJ; Kim SH; Na JI; Kim MS; Park GH; Park I; Kim K; Lim W; Lee JH; Chang SE
PLoS Med; 2020 Nov; 17(11):e1003381. PubMed ID: 33237903
[TBL] [Abstract][Full Text] [Related]
38. Dermatologist-level classification of malignant lip diseases using a deep convolutional neural network.
Cho SI; Sun S; Mun JH; Kim C; Kim SY; Cho S; Youn SW; Kim HC; Chung JH
Br J Dermatol; 2020 Jun; 182(6):1388-1394. PubMed ID: 31449661
[TBL] [Abstract][Full Text] [Related]
39. GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification.
Tang P; Liang Q; Yan X; Xiang S; Zhang D
IEEE J Biomed Health Inform; 2020 Oct; 24(10):2870-2882. PubMed ID: 32142460
[TBL] [Abstract][Full Text] [Related]
40. Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.
Ji H; Li J; Zhu X; Fan L; Jiang W; Chen Y
Skin Res Technol; 2024 Apr; 30(4):e13698. PubMed ID: 38634154
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
