Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
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Arnold, 2014, Trends in incidence and predictions of cutaneous melanoma across Europe up to 2015., J Eur Acad Dermatol Venereol, 28, 1170, 10.1111/jdv.2014.28.issue-9
Shellenberger, 2016, Melanoma screening: a plan for improving early detection., Ann Med, 48, 142, 10.3109/07853890.2016.1145795
Argenziano, 2012, Accuracy in melanoma detection: a 10-year multicenter survey., J Am Acad Dermatol, 67, 54, 10.1016/j.jaad.2011.07.019
Bafounta, 2001, Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests., Arch Dermatol, 137, 1343, 10.1001/archderm.137.10.1343
Vestergaard, 2008, Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting., Br J Dermatol, 159, 669
Kittler, 2002, Diagnostic accuracy of dermoscopy., Lancet Oncol, 3, 159, 10.1016/S1470-2045(02)00679-4
Pehamberger, 1987, In vivo epiluminescence microscopy of pigmented skin lesions, I: pattern analysis of pigmented skin lesions., J Am Acad Dermatol, 17, 571, 10.1016/S0190-9622(87)70239-4
Stolz, 1994, ABCD rule of dermatoscopy: a new practical method for early recognition of malignant melanoma., Eur J Dermatol, 4, 521
Menzies, 1996, Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features., Arch Dermatol, 132, 1178, 10.1001/archderm.1996.03890340038007
Argenziano, 1998, Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis., Arch Dermatol, 134, 1563, 10.1001/archderm.134.12.1563
Okur, 2018, A survey on automated melanoma detection., Eng Appl Artif Intell, 73, 50, 10.1016/j.engappai.2018.04.028
Haenssle, 2018, Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists., Ann Oncol, 29, 1836, 10.1093/annonc/mdy166
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks., Nature, 542, 115, 10.1038/nature21056
Tschandl, Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features, Br J Dermatol
Yamashita, 2018, Convolutional neural networks: an overview and application in radiology., Insights Imaging, 9, 611, 10.1007/s13244-018-0639-9
Du, 2018, Application of artificial intelligence in ophthalmology., Int J Ophthalmol, 11, 1555
World Medical Association, 2013, World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects., JAMA, 310, 2191, 10.1001/jama.2013.281053
Montavon, 2018, Methods for interpreting and understanding deep neural networks., Digit Signal Process, 73, 1, 10.1016/j.dsp.2017.10.011
Brinker, 2018, Skin cancer classification using convolutional neural networks: systematic review., J Med Internet Res, 20, e11936, 10.2196/11936
Marchetti, 2018, 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., J Am Acad Dermatol, 78, 270, 10.1016/j.jaad.2017.08.016
Han, 2018, Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm., J Invest Dermatol, 138, 1529, 10.1016/j.jid.2018.01.028
Salido, 2018, Using deep learning to detect melanoma in dermoscopy images., Int J Mach Learn Comput, 8
SunJ, BinderA. Comparison of deep learning architectures for H&E histopathology images. Paper presented at: IEEE Conference on Big Data and Analytics (ICBDA); November 16, 2017; Kuching, Malaysia. https://ieeexplore.ieee.org/document/8284105. Accessed November 7, 2018.
SzegedyC, LiuW, JiaY, . Going deeper with convolutions. Paper presented at: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 8, 2015; Boston, MA. https://arxiv.org/pdf/1409.4842.pdf. Accessed November 7, 2018.
SimonyanK, VedaldiA, ZissermanA. Deep inside convolutional networks: visualising image classification models and saliency maps. https://arxiv.org/abs/1312.6034. Published December 20, 2013. Accessed April 5, 2019.
MishraNK, CelebiME. An overview of melanoma detection in dermoscopy images using image processing and machine learning. https://arxiv.org/ftp/arxiv/papers/1601/1601.07843.pdf. Published January 27, 2016. Accessed November 7, 2018.
JafariMH, KarimiN, Nasr-EsfahaniE, . Skin lesion segmentation in clinical images using deep learning. Paper presented at: 2016 23rd International Conference on Pattern Recognition (ICPR); December 4, 2016; Cancun, Mexico. https://ieeexplore.ieee.org/document/7899656. Published April 24, 2017. Accessed November 7, 2018.
YoshidaT, CelebiME, SchaeferG, IyatomiH. Simple and effective pre-processing for automated melanoma discrimination based on cytological findings. Paper presented at: IEEE International Conference on Big Data; December 6, 2016; Washington, DC. https://ieeexplore.ieee.org/document/7841005. Published February 6, 2017. Accessed November 7, 2018.
SultanaNN, PuhanN. Recent deep learning methods for melanoma detection: a review. In: Ghosh D, Giri D, Mohapatra R, Savas E, Sakurai K, Singh L, eds. Mathematics and Computing: ICMC 2018: Communications in Computer and Information Science. Singapore: Springer; 2018;834:118-132. https://www.researchgate.net/publication/324502578_Recent_Deep_Learning_Methods_for_Melanoma_Detection_A_Review. Published April 2018. Accessed November 7, 2018.