428 related articles for article (PubMed ID: 29513718)
21. [Dermoscopic pattern analysis of acral melanocytic nevi].
Wawrzynkiewicz M; Wozniak W; Wojas-Pelc A; Chlebick M; Ogiela J
Przegl Lek; 2013; 70(11):911-5. PubMed ID: 24697028
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. 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]
24. A comparative study of deep learning architectures on melanoma detection.
Hosseinzadeh Kassani S; Hosseinzadeh Kassani P
Tissue Cell; 2019 Jun; 58():76-83. PubMed ID: 31133249
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis.
Gonzalez-Diaz I
IEEE J Biomed Health Inform; 2019 Mar; 23(2):547-559. PubMed ID: 29994788
[TBL] [Abstract][Full Text] [Related]
27. Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.
Maron RC; Utikal JS; Hekler A; Hauschild A; Sattler E; Sondermann W; Haferkamp S; Schilling B; Heppt MV; Jansen P; Reinholz M; Franklin C; Schmitt L; Hartmann D; Krieghoff-Henning E; Schmitt M; Weichenthal M; von Kalle C; Fröhling S; Brinker TJ
J Med Internet Res; 2020 Sep; 22(9):e18091. PubMed ID: 32915161
[TBL] [Abstract][Full Text] [Related]
28. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.
Brinker TJ; Hekler A; Enk AH; Klode J; Hauschild A; Berking C; Schilling B; Haferkamp S; Schadendorf D; Fröhling S; Utikal JS; von Kalle C;
Eur J Cancer; 2019 Apr; 111():148-154. PubMed ID: 30852421
[TBL] [Abstract][Full Text] [Related]
29. Early detection of acral melanoma: A review of clinical, dermoscopic, histopathologic, and molecular characteristics.
Darmawan CC; Jo G; Montenegro SE; Kwak Y; Cheol L; Cho KH; Mun JH
J Am Acad Dermatol; 2019 Sep; 81(3):805-812. PubMed ID: 30731177
[TBL] [Abstract][Full Text] [Related]
30. Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin.
Iyatomi H; Oka H; Celebi ME; Ogawa K; Argenziano G; Soyer HP; Koga H; Saida T; Ohara K; Tanaka M
J Invest Dermatol; 2008 Aug; 128(8):2049-54. PubMed ID: 18323788
[TBL] [Abstract][Full Text] [Related]
31. Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images.
Kaya S; Bayraktar M; Kockara S; Mete M; Halic T; Field HE; Wong HK
BMC Bioinformatics; 2016 Oct; 17(Suppl 13):367. PubMed ID: 27766942
[TBL] [Abstract][Full Text] [Related]
32. Early acral melanoma in situ: correlation between the parallel ridge pattern on dermoscopy and microscopic features.
Ishihara Y; Saida T; Miyazaki A; Koga H; Taniguchi A; Tsuchida T; Toyama M; Ohara K
Am J Dermatopathol; 2006 Feb; 28(1):21-7. PubMed ID: 16456320
[TBL] [Abstract][Full Text] [Related]
33. Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.
Winkler JK; Sies K; Fink C; Toberer F; Enk A; Abassi MS; Fuchs T; Haenssle HA
Eur J Cancer; 2021 Mar; 145():146-154. PubMed ID: 33465706
[TBL] [Abstract][Full Text] [Related]
34. Review of medical image recognition technologies to detect melanomas using neural networks.
Efimenko M; Ignatev A; Koshechkin K
BMC Bioinformatics; 2020 Sep; 21(Suppl 11):270. PubMed ID: 32921304
[TBL] [Abstract][Full Text] [Related]
35. Border detection in dermoscopy images using hybrid thresholding on optimized color channels.
Garnavi R; Aldeen M; Celebi ME; Varigos G; Finch S
Comput Med Imaging Graph; 2011 Mar; 35(2):105-15. PubMed ID: 20832992
[TBL] [Abstract][Full Text] [Related]
36. Diagnostic accuracy of dermatofluoroscopy in cutaneous melanoma detection: results of a prospective multicentre clinical study in 476 pigmented lesions.
Forschner A; Keim U; Hofmann M; Spänkuch I; Lomberg D; Weide B; Tampouri I; Eigentler T; Fink C; Garbe C; Haenssle HA
Br J Dermatol; 2018 Aug; 179(2):478-485. PubMed ID: 29569229
[TBL] [Abstract][Full Text] [Related]
37. Key points in dermoscopic differentiation between early acral melanoma and acral nevus.
Saida T; Koga H; Uhara H
J Dermatol; 2011 Jan; 38(1):25-34. PubMed ID: 21175752
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Convolutional neural networks for the detection of malignant melanoma in dermoscopy images.
Kwiatkowska D; Kluska P; Reich A
Postepy Dermatol Alergol; 2021 Jun; 38(3):412-420. PubMed ID: 34377121
[TBL] [Abstract][Full Text] [Related]
40. Dermoscopy patterns of nevi associated with melanoma.
Kolm I; French L; Braun RP
G Ital Dermatol Venereol; 2010 Feb; 145(1):99-110. PubMed ID: 20197749
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
