Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer

Brynjolfsson, 2017, What's driving the machine learning explosion?, Harvard Business Rev, 18, 3 Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Ercal, 1994, Neural network diagnosis of malignant melanoma from color images, IEEE Trans Biomed Eng, 41, 837, 10.1109/10.312091 Rubegni, 2002, Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study, J Invest Dermatol, 119, 471, 10.1046/j.1523-1747.2002.01835.x Stanley, 2003, A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images, Comput Med Imaging Graph, 27, 387, 10.1016/S0895-6111(03)00030-2 Maglogiannis, 2004, Characterization of digital medical images utilizing support vector machines, BMC Med Inform Decis Mak, 4, 4, 10.1186/1472-6947-4-4 Maglogiannis, 2005, An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images, IEEE Trans Inf Technol Biomed, 9, 86, 10.1109/TITB.2004.837859 Stoecker, 2005, Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color, Skin Res Technol, 11, 179, 10.1111/j.1600-0846.2005.00117.x Stanley, 2007, A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images, Skin Res Technol, 13, 62, 10.1111/j.1600-0846.2007.00192.x Surowka, 2007, Different learning paradigms for the classification of melanoid skin lesions using wavelets, 3136 Iyatomi, 2008, An Internet-based melanoma screening system with acral volar lesion support, 5156 Ruiz, 2008, A cooperative approach for the diagnosis of the melanoma, 2008, 5144 Khan, 2009, Fuzzy logic techniques for blotch feature evaluation in dermoscopy images, Comput Med Imaging Graph, 33, 50, 10.1016/j.compmedimag.2008.10.001 Gilmore, 2010, A support vector machine for decision support in melanoma recognition, Exp Dermatol, 19, 830, 10.1111/j.1600-0625.2010.01112.x Surowka, 2010, Symbolic learning supporting early diagnosis of melanoma, 2010, 4104 Tenenhaus, 2010, Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions, Skin Res Technol, 16, 85, 10.1111/j.1600-0846.2009.00385.x Cheng, 2012, Automatic telangiectasia analysis in dermoscopy images using adaptive critic design, Skin Res Technol, 18, 389, 10.1111/j.1600-0846.2011.00584.x Garnavi, 2012, Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis, IEEE Trans Inf Technol Biomed, 16, 1239, 10.1109/TITB.2012.2212282 Liu, 2012, Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas, Med Biol Eng Comput, 50, 503, 10.1007/s11517-012-0895-7 Chang, 2013, Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study, PLoS One, 8, e76212, 10.1371/journal.pone.0076212 Cheng, 2013, Automatic dirt trail analysis in dermoscopy images, Skin Res Technol, 19, e20, 10.1111/j.1600-0846.2011.00602.x Liu, 2013, Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma, Br J Dermatol, 169, 1034, 10.1111/bjd.12550 Lingala, 2014, Fuzzy logic color detection: blue areas in melanoma dermoscopy images, Comput Med Imaging Graph, 38, 403, 10.1016/j.compmedimag.2014.03.007 Riaz, 2014, Detecting melanoma in dermoscopy images using scale adaptive local binary patterns, 2014, 6758 Sabouri, 2014, A cascade classifier for diagnosis of melanoma in clinical images, 2014, 6748 Souza, 2014, Nevus and melanoma paraconsistent classification, Stud Health Technol Inform, 207, 244 Ferris, 2015, Computer-aided classification of melanocytic lesions using dermoscopic images, J Am Acad Dermatol, 73, 769, 10.1016/j.jaad.2015.07.028 Kaur, 2015, Real-time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location, Skin Res Technol, 21, 466, 10.1111/srt.12216 Rastgoo, 2015, Automatic differentiation of melanoma from dysplastic nevi, Comput Med Imaging Graph, 43, 44, 10.1016/j.compmedimag.2015.02.011 Shimizu, 2015, Four-class classification of skin lesions with task decomposition strategy, IEEE Trans Biomed Eng, 62, 274, 10.1109/TBME.2014.2348323 Jafari, 2016, Automatic detection of melanoma using broad extraction of features from digital images, 2016, 1357 Jaworek-Korjakowska, 2016, Computer-aided diagnosis of micro-malignant melanoma lesions applying support vector machines, Biomed Res Int, 2016, 4381972, 10.1155/2016/4381972 Jaworek-Korjakowska, 2016, Automatic classification of specific melanocytic lesions using artificial intelligence, Biomed Res Int, 2016, 8934242, 10.1155/2016/8934242 Kefel, 2016, Adaptable texture-based segmentation by variance and intensity for automatic detection of semitranslucent and pink blush areas in basal cell carcinoma, Skin Res Technol, 22, 412, 10.1111/srt.12281 Nasr-Esfahani, 2016, Melanoma detection by analysis of clinical images using convolutional neural network, 2016, 1373 Premaladha, 2016, Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms, J Med Syst, 40, 96, 10.1007/s10916-016-0460-2 Sabbaghi, 2016, A deep bag-of-features model for the classification of melanomas in dermoscopy images, 2016, 1369 Gareau, 2017, Digital imaging biomarkers feed machine learning for melanoma screening, Exp Dermatol, 26, 615, 10.1111/exd.13250 Munia, 2017, Automatic diagnosis of melanoma using linear and nonlinear features from digital image, 2017, 4281 Xie, 2017, Melanoma classification on dermoscopy images using a neural network ensemble model, IEEE Trans Med Imaging, 36, 849, 10.1109/TMI.2016.2633551 Yu, 2017, Automated melanoma recognition in dermoscopy images via very deep residual networks, IEEE Trans Med Imaging, 36, 994, 10.1109/TMI.2016.2642839 Gautam, 2018, Machine learning-based diagnosis of melanoma using macro images, Int J Numer Method Biomed Eng, 34, e2953, 10.1002/cnm.2953 Gilmore, 2018, Automated decision support in melanocytic lesion management, PLoS One, 13, e0203459, 10.1371/journal.pone.0203459 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 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 Li, 2018, Skin lesion analysis towards melanoma detection using deep learning network, Sensors (Basel), 18, E556, 10.3390/s18020556 Mahbod, 2019, Fusing fine-tuned deep features for skin lesion classification, Comput Med Imaging Graph, 71, 19, 10.1016/j.compmedimag.2018.10.007 Rebouças Filho, 2018, Automatic histologically-closer classification of skin lesions, Comput Med Imaging Graph, 68, 40, 10.1016/j.compmedimag.2018.05.004 Wahba, 2018, A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification, Comput Methods Programs Biomed, 165, 163, 10.1016/j.cmpb.2018.08.009 Walker, 2019, Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: laboratory and prospective observational studies, EBioMedicine, 40, 176, 10.1016/j.ebiom.2019.01.028 Yu, 2018, Acral melanoma detection using a convolutional neural network for dermoscopy images, PLoS One, 13, e0193321, 10.1371/journal.pone.0193321 Yu, 2019, Melanoma recognition in dermoscopy images via aggregated deep convolutional features, IEEE Trans Biomed Eng, 66, 1006, 10.1109/TBME.2018.2866166 Zakhem, 2018, How should artificial intelligence screen for skin cancer and deliver diagnostic predictions to patients?, JAMA Dermatol, 154, 1383, 10.1001/jamadermatol.2018.2714 Ng Tschandl, 2018, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions, Sci Data, 5, 180161, 10.1038/sdata.2018.161 Cormier, 2006, Ethnic differences among patients with cutaneous melanoma, Arch Intern Med, 166, 1907, 10.1001/archinte.166.17.1907 Ward-Peterson, 2016, Association between race/ethnicity and survival of melanoma patients in the united states over 3 decades: a secondary analysis of SEER data, Medicine, 95, e3315, 10.1097/MD.0000000000003315 Adamson, 2018, Machine learning and health care disparities in dermatology, JAMA Dermatol, 154, 1247, 10.1001/jamadermatol.2018.2348 Abadi, 2016, TensorFlow: a system for large-scale machine learning Szegedy C, Sergey I, Vanhoucke V. Inception-v4, Inception-ResNet and the impact of residual connections on learning. Paper presented at: The Thirteenth Association for the Advancement of Artificial Intelligence (AAAI) Conference. Phoenix, AZ. February 12-17, 2016. Boyd DataRobot, 2019 Google 2019