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For: Gilmore S, Hofmann-Wellenhof R, Soyer HP. A support vector machine for decision support in melanoma recognition. Exp Dermatol 2010;19:830-5. [PMID: 20629732 DOI: 10.1111/j.1600-0625.2010.01112.x] [Cited by in Crossref: 39] [Cited by in F6Publishing: 24] [Article Influence: 3.3] [Reference Citation Analysis]
Number Citing Articles
1 Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2014;13:8-17. [PMID: 25750696 DOI: 10.1016/j.csbj.2014.11.005] [Cited by in Crossref: 981] [Cited by in F6Publishing: 530] [Article Influence: 122.6] [Reference Citation Analysis]
2 Gilmore SJ. Automated decision support in melanocytic lesion management. PLoS One 2018;13:e0203459. [PMID: 30192804 DOI: 10.1371/journal.pone.0203459] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
3 Zhou Y, Song Z. Melanoma Diagnosis with Multiple Decision Trees. In: Scharcanski J, Celebi ME, editors. Computer Vision Techniques for the Diagnosis of Skin Cancer. Berlin: Springer Berlin Heidelberg; 2014. pp. 267-82. [DOI: 10.1007/978-3-642-39608-3_10] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.1] [Reference Citation Analysis]
4 Gilmore S. Melanoma screening: Informing public health policy with quantitative modelling. PLoS One 2017;12:e0182349. [PMID: 28945758 DOI: 10.1371/journal.pone.0182349] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 1.6] [Reference Citation Analysis]
5 Zakhem GA, Fakhoury JW, Motosko CC, Ho RS. Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer: A systematic review. J Am Acad Dermatol. 2020;. [PMID: 31972254 DOI: 10.1016/j.jaad.2020.01.028] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
6 Baig R, Bibi M, Hamid A, Kausar S, Khalid S. Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images - A Review. Curr Med Imaging. 2020;16:513-533. [PMID: 32484086 DOI: 10.2174/1573405615666190129120449] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 8.0] [Reference Citation Analysis]
7 Hames SC, Sinnya S, Tan JM, Morze C, Sahebian A, Soyer HP, Prow TW. Automated detection of actinic keratoses in clinical photographs. PLoS One 2015;10:e0112447. [PMID: 25615930 DOI: 10.1371/journal.pone.0112447] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 1.9] [Reference Citation Analysis]
8 Majtner T, Yildirim-yayilgan S, Hardeberg JY. Optimised deep learning features for improved melanoma detection. Multimed Tools Appl 2019;78:11883-903. [DOI: 10.1007/s11042-018-6734-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
9 Pérez E, Reyes O, Ventura S. Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study. Med Image Anal 2021;67:101858. [PMID: 33129155 DOI: 10.1016/j.media.2020.101858] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
10 Aberg P, Birgersson U, Elsner P, Mohr P, Ollmar S. Electrical impedance spectroscopy and the diagnostic accuracy for malignant melanoma. Exp Dermatol 2011;20:648-52. [PMID: 21539620 DOI: 10.1111/j.1600-0625.2011.01285.x] [Cited by in Crossref: 53] [Cited by in F6Publishing: 37] [Article Influence: 4.8] [Reference Citation Analysis]
11 Zhou Y, Song Z. Binary Decision Trees for Melanoma Diagnosis. In: Zhou Z, Roli F, Kittler J, editors. Multiple Classifier Systems. Berlin: Springer Berlin Heidelberg; 2013. pp. 374-85. [DOI: 10.1007/978-3-642-38067-9_33] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.2] [Reference Citation Analysis]
12 Mete M, Sirakov NM. Dermoscopic diagnosis of melanoma in a 4D space constructed by active contour extracted features. Computerized Medical Imaging and Graphics 2012;36:572-9. [DOI: 10.1016/j.compmedimag.2012.06.002] [Cited by in Crossref: 19] [Cited by in F6Publishing: 9] [Article Influence: 1.9] [Reference Citation Analysis]
13 Masood A, Al-Jumaily AA. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J Biomed Imaging 2013;2013:323268. [PMID: 24575126 DOI: 10.1155/2013/323268] [Cited by in Crossref: 127] [Cited by in F6Publishing: 53] [Article Influence: 14.1] [Reference Citation Analysis]
14 Dinnes J, Deeks JJ, Chuchu N, Matin RN, Wong KY, Aldridge RB, Durack A, Gulati A, Chan SA, Johnston L, Bayliss SE, Leonardi-Bee J, Takwoingi Y, Davenport C, O'Sullivan C, Tehrani H, Williams HC; Cochrane Skin Cancer Diagnostic Test Accuracy Group. Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults. Cochrane Database Syst Rev 2018;12:CD011901. [PMID: 30521688 DOI: 10.1002/14651858.CD011901.pub2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 2.5] [Reference Citation Analysis]
15 Korotkov K, Garcia R. Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine 2012;56:69-90. [DOI: 10.1016/j.artmed.2012.08.002] [Cited by in Crossref: 212] [Cited by in F6Publishing: 85] [Article Influence: 21.2] [Reference Citation Analysis]
16 Surówka G, Ogorzalek M. Resolution invariant wavelet features of melanoma studied by SVM classifiers. PLoS One 2019;14:e0211318. [PMID: 30726260 DOI: 10.1371/journal.pone.0211318] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 1.3] [Reference Citation Analysis]
17 Rastgoo M, Garcia R, Morel O, Marzani F. Automatic differentiation of melanoma from dysplastic nevi. Computerized Medical Imaging and Graphics 2015;43:44-52. [DOI: 10.1016/j.compmedimag.2015.02.011] [Cited by in Crossref: 55] [Cited by in F6Publishing: 23] [Article Influence: 7.9] [Reference Citation Analysis]
18 Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. JAMA Dermatol 2019;155:1291-9. [PMID: 31215969 DOI: 10.1001/jamadermatol.2019.1375] [Cited by in Crossref: 29] [Cited by in F6Publishing: 15] [Article Influence: 9.7] [Reference Citation Analysis]
19 Dinnes J, Deeks JJ, Chuchu N, Ferrante di Ruffano L, Matin RN, Thomson DR, Wong KY, Aldridge RB, Abbott R, Fawzy M, Bayliss SE, Grainge MJ, Takwoingi Y, Davenport C, Godfrey K, Walter FM, Williams HC; Cochrane Skin Cancer Diagnostic Test Accuracy Group. Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database Syst Rev 2018;12:CD011902. [PMID: 30521682 DOI: 10.1002/14651858.CD011902.pub2] [Cited by in Crossref: 27] [Cited by in F6Publishing: 25] [Article Influence: 6.8] [Reference Citation Analysis]
20 Ferrante di Ruffano L, Takwoingi Y, Dinnes J, Chuchu N, Bayliss SE, Davenport C, Matin RN, Godfrey K, O'Sullivan C, Gulati A, Chan SA, Durack A, O'Connell S, Gardiner MD, Bamber J, Deeks JJ, Williams HC; Cochrane Skin Cancer Diagnostic Test Accuracy Group. Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev 2018;12:CD013186. [PMID: 30521691 DOI: 10.1002/14651858.CD013186] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 5.0] [Reference Citation Analysis]
21 Boyce Z, Gilmore S, Xu C, Soyer H. The Remote Assessment of Melanocytic Skin Lesions: A Viable Alternative to Face-to-Face Consultation. Dermatology 2011;223:244-50. [DOI: 10.1159/000333363] [Cited by in Crossref: 21] [Cited by in F6Publishing: 19] [Article Influence: 1.9] [Reference Citation Analysis]
22 Szyc Ł, Hillen U, Scharlach C, Kauer F, Garbe C. Diagnostic Performance of a Support Vector Machine for Dermatofluoroscopic Melanoma Recognition: The Results of the Retrospective Clinical Study on 214 Pigmented Skin Lesions. Diagnostics (Basel) 2019;9:E103. [PMID: 31450697 DOI: 10.3390/diagnostics9030103] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
23 Rubegni P, Tognetti L, Argenziano G, Nami N, Brancaccio G, Cinotti E, Miracco C, Fimiani M, Cevenini G. A risk scoring system for the differentiation between melanoma with regression and regressing nevi. J Dermatol Sci 2016;83:138-44. [PMID: 27157925 DOI: 10.1016/j.jdermsci.2016.04.012] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
24 Majtner T, Yildirim-yayilgan S, Hardeberg JY. Efficient Melanoma Detection Using Texture-Based RSurf Features. In: Campilho A, Karray F, editors. Image Analysis and Recognition. Cham: Springer International Publishing; 2016. pp. 30-7. [DOI: 10.1007/978-3-319-41501-7_4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]