[1] T Tarver. Cancer Facts & Figures 2012. American Cancer Society (ACS). Journal of Consumer Health on the Internet, 2012, 16(3):366-367.
[2] G M Weber, K D Mandl, I S Kohane. Finding the missing link for big biomedical data. Jama, 2014, 311(24):2479.
[3] D-M Filimon, A Albu. Skin diseases diagnosis using artificial neural networks. 2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, 2014:189-194, https://doi.org/10.1109/SACI.2014.6840059
[4] A Serener, S Serte. Geographic variation and ethnicity in diabetic retinopathy detection via deeplearning. Turkish Journal of Electrical Engineering and Computer Sciences, 2020, 28(2):664-678.
[5] B Zhang, Y Luo, L Ma, et al. 3D bioprinting:an emerging technology full of opportunities and challenges. Bio-Design and Manufacturing, 2018, 1(1):2-13.
[6] S Pathan, K G Prabhu, P Siddalingaswamy. Techniques and algorithms for computer aided diagnosis of pigmented skin lesions-A review. Biomedical Signal Processing and Control, 2018, 39:237-262.
[7] A Paradisi, S Tabolli, B Didona, et al. Markedly reduced incidence of melanoma and nonmelanoma skin cancer in a nonconcurrent cohort of 10,040 patients with vitiligo. Journal of the American Academy of Dermatology, 2014, 71(6):1110-1116.
[8] M E Celebi, Q Wen, H Iyatomi, et al. A state-of-the-art survey on lesion border detection in dermoscopy images. Dermoscopy Image Analysis, 2015:97-129.
[9] A Esteva, B Kuprel, R A Novoa, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639):115.
[10] A Steiner, H Pehamberger, K Wolff. Improvement of the diagnostic accuracy in pigmented skin lesions by epiluminescent light microscopy. Anticancer Research, 1987, 7(3):433-434.
[11] S Joseph, J R Panicker. Skin lesion analysis system for melanoma detection with an effective hair segmentation method. 2016 International Conference in Information Science (ICIS), IEEE, 2016:91-96, https://doi.org/10.1109/infosci.2016.7845307
[12] P Zaenker, L Lo, R Pearce, et al. A diagnostic autoantibody signature for primary cutaneous melanoma. Oncotarget, 2018, 9(55):30539.
[13] C Barata, M Ruela, M Francisco, et al. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 2014, 8(3):965-979.
[14] T Vos, C Allen, M Arora, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015:A systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 2016, 388(10053):1545-1602.
[15] P Wang, S Wang. Computer-aided CT image processing and modeling method for tibia microstructure. Bio-Design and Manufacturing, 2020, 3(1):71-82.
[16] Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 2015, 521(7553):436.
[17] Y LeCun, L Bottou, Y Bengio, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[18] O Russakovsky, J Deng, H Su, et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3):211-252.
[19] A Krizhevsky, I Sutskever, G E Hinton. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, 25:1097-1105.
[20] M D Zeiler, R Fergus. Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer, Cham, 2014:818-833.
[21] K Simonyan, A Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[22] C Szegedy, W Liu, Y Jia, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015:1-9.. https://doi.org/10.1109/CVPR.2015.7298594
[23] K He, X Zhang, S Ren, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016:770-778, https://doi.org/10.1109/CVPR.2016.90
[24] B Alipanahi, A Delong, M T Weirauch, et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 2015, 33(8):831.
[25] J Zhou, O G Troyanskaya. Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 2015, 12(10):931.
[26] A Shademan, R S Decker, J D Opfermann, et al. Supervised autonomous robotic soft tissue surgery. Science Translational Medicine, 2016, 8(337):337ra64-337ra64.
[27] S Kaymak, A Serener. Automated age-related macular degeneration and diabetic macular edema detection on OCT images using deep learning. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, 2018, https://doi.org/10.1109/ICCP.2018.8516635
[28] C Szegedy, V Vanhoucke, S Ioffe, et al. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016:2818-2826, https://doi.org/10.1109/CVPR.2016.308
[29] M Abadi, A Agarwal, P Barham, et al. TensorFlow:Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.
[30] Y Jia, E Shelhamer, J Donahue, et al. Caffe:Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, 2014:675-678, https://doi.org/10.1145/2647868.2654889
[31] F Bastien, P Lamblin, R Pascanu, et al. Theano:new features and speed improvements. arXiv preprint arXiv:1211.5590, 2012.
[32] H Choi. Deep learning in nuclear medicine and molecular imaging:current perspectives and future directions. Nuclear Medicine and Molecular Imaging, 2018, 52(2):109-118.
[33] N Tajbakhsh, J Y Shin, S R Gurudu, et al. Convolutional neural networks for medical image analysis:Full training or fine tuning? IEEE Transactions on Medical Imaging, 2016, 35(5):1299-1312.
[34] Y Xu, T Mo, Q Feng, et al. Deep learning of feature representation with multiple instance learning for medical image analysis. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014:1626-1630, https://doi.org/10.1109/ICASSP.2014.6853873
[35] E Long, H Lin, Z Liu, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature Biomedical Engineering, 2017, 1(2):0024.
[36] P Rajpurkar, J Irvin, K Zhu, et al. Chexnet:Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 2017.
[37] V Gulshan, L Peng, M Coram, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 2016, 316(22):2402-2410.
[38] S F Weng, J Reps, J Kai, et al. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS One, 2017, 12(4):e0174944.
[39] H C Hazlett, H Gu, B C Munsell, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641):348.
[40] S Sarraf, G Tofighi. Classification of alzheimer's disease structural MRI data by deep learning convolutional neural networks. arXiv preprint arXiv:1607.06583, 2016.
[41] N Amoroso, M La Rocca, S Bruno, et al. Brain structural connectivity atrophy in Alzheimer's disease. arXiv preprint arXiv:1709.02369, 2017.
[42] L Rosado, M Ferreira. A prototype for a mobile-based system of skin lesion analysis using supervised classification. 2013 2nd Experiment International Conference (exp. at'13), IEEE, 2013:156-157, https://doi.org/10.1109/ExpAt.2013.6703051
[43] J Hagerty, J Stanley, H Almubarak, et al. Deep learning and handcrafted method fusion:Higher diagnostic accuracy for melanoma dermoscopy images. IEEE Journal of Biomedical and Health Informatics, 2019:1-1, https://doi.org/10.1109/JBHI.2019.2891049
[44] Andres, Diaz-Pinto, Sandra, et al. CNNs for automatic glaucoma assessment using fundus images:an extensive validation. Biomedical Engineering Online, 2019, 18(1), https://doi.org/10.1186/s12938-019-0649-y
[45] Y Li, L Shen. Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 2018, 18(2):556.
[46] Y Gurovich, Y Hanani, O Bar, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nature Medicine, 2019, 25(1):60.
[47] S S Han, M S Kim, W Lim, et al. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology, 2018, 138(7):1529-1538.
[48] H Haenssle, C Fink, R Schneiderbauer, et al. Man against machine:Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 2018, 29(8):1836-1842, 2018.
[49] C Mehanian, M Jaiswal, C Delahunt, et al. Computer-automated malaria diagnosis and quantitation using convolutional neural networks. 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), IEEE, https://doi.org/10.1109/ICCVW.2017.22.
[50] M Poostchi, K Silamut, R Maude, et al. Image analysis and machine learning for detecting malaria. Translational Research the Journal of Laboratory & Clinical Medicine, 2018, 194:36-55.
[51] Z I Attia, S Kapa, F Lopez-Jimenez, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nature Medicine, 2019, 25(1):70.
[52] A Y Hannun, P Rajpurkar, M Haghpanahi, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 2019, 25(1):65.
[53] J Zhang, Y Xie, Y Xia, et al. Attention residual learning for skin lesion classification. IEEE Transactions on Medical Imaging, 2019:1-1, https://doi.org/10.1109/TMI.2019.2893944
[54] Y Fujisawa, Y Otomo, Y Ogata, et al. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. British Journal of Dermatology, 2019, 180(61), https://doi.org/10.1111/bjd.16924
[55] A Rezvantalab, H Safigholi, S Karimijeshni. Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. arXiv preprint arXiv:1810.10348, 2018.
[56] K Yasaka, H Akai, A Kunimatsu, et al. Deep learning with convolutional neural network in radiology. Japanese Journal of Radiology, 2018:1-16.
[57] A Khamparia, P K Singh, P Rani, et al. An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning. Transactions on Emerging Telecommunications Technologies, 2020.
[58] D Gutman, N C Codella, E Celebi, et al. Skin lesion analysis toward melanoma detection:A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397, 2016.
[59] L Bi, J Kim, E Ahn, et al. Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint arXiv:1703.04197, 2017.
[60] S Serte, H Demirel. Gabor wavelet-based deep learning for skin lesion classification. Computers in Biology and Medicine, 2019, 113:103423.
[61] N C Codella, Q-B Nguyen, S Pankanti, et al. Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development, 2017, 61(4/5):5:1-5:15.
[62] L Yu, H Chen, Q Dou, et al. Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging, 2017, 36(4):994-1004.
[63] X Fan, M Dai, C Liu, et al. Effect of image noise on the classification of skin lesions using deep convolutional neural networks. Tsinghua Science and Technology, 2020, 25(3):425-434.
[64] M Combalia, N Codella, V Rotemberg, et al. BCN20000:Dermoscopic Lesions in the Wild, arXiv preprint arXiv:1908.02288, 2019.
[65] P Tschandl, C Rosendahl, H Kittler. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 2018, 5(1):1-9.
[66] ISIC Project-ISIC Archive. Accessed:May 23, 2021. Available:https://www.isic-archive.com.
[67] N Codella, D Gutman, M E Celebi, et al. Skin lesion analysis toward melanoma detection:A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC), 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018:168-172, https://doi.org/10.1109/ISBI.2018.8363547
[68] Y Yang, Y Ge, L Guo, et al. Development and validation of two artificial intelligence models for diagnosing benign, pigmented facial skin lesions. Skin Research and Technology, 2020, https://doi.org/10.1111/srt.12911
[69] Derm101 Image Library. Accessed:Jan. 12, 2019. Available:https://www.derm101.com/image_librarv/.
[70] Dermnet-Skin Disease Altas. Accessed:Dec. 31, 2018. Available:http://www.dermnet.com/.
[71] H Mhaske, D Phalke. Melanoma skin cancer detection and classification based on supervised and unsupervised learning. 2013 International Conference on Circuits, Controls and Communications (CCUBE), 2013:1-5, https://doi.org/10.1109/CCUBE.2013.6718539
[72] I G Díaz. Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. IEEE Journal of Biomedical and Health Informatics, 2017, https://doi.org/10.1109/JBHI.2018.2806962
[73] O Abuzaghleh, B D Barkana, M Faezipour. Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. IEEE Long Island Systems, Applications and Technology (LISAT) Conference, 2014:1-6, https://doi.org/10.1109/LISAT.2014.6845199
[74] A Pennisi, D D Bloisi, D Nardi, et al. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection. Computerized Medical Imaging and Graphics, 2016, 52:89-103.
[75] D D Gómez, C Butakoff, B K Ersboll, et al. Independent histogram pursuit for segmentation of skin lesions. IEEE Transactions on Biomedical Engineering, 2008, 55(1):157-161.
[76] S Kaymak, P Esmaili, A Serener. Deep learning for two-step classification of malignant pigmented skin lesions. 2018 14th Symposium on Neural Networks and Applications (NEUREL), 2018:1-6.
[77] H Balazs. Skin lesion classification with ensembles of deep convolutional neural networks. Journal of Biomedical Informatics, 2018, 86:S1532046418301618-.
[78] A Mahbod, G Schaefer, C Wang, et al. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Computer Methods and Programs in Biomedicine, 2020, 193:105475.
[79] A G Howard. Some improvements on deep convolutional neural network based image classification, arXiv preprint arXiv:1312.5402, 2013.
[80] W Paja, M Wrzesień. Melanoma important features selection using random forest approach. 2013 6th International Conference on Human System Interactions (HSI), 2013:415-418, https://doi.org/10.1109/HSI.2013.6577857
[81] F Nachbar, W Stolz, T Merkle, et al. The ABCD rule of dermatoscopy:High prospective value in the diagnosis of doubtful melanocytic skin lesions. Journal of the American Academy of Dermatology, 1994, 30(4):551-559.
[82] M Nasir, M Attique Khan, M Sharif, et al. An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microscopy Research and Technique, 2018, 81(6):528-543.
[83] D G Lowe. Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image:US, US6711293. 2004-3-23.
[84] N Dalal, B Triggs. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, 1:886-893, https://doi.org/10.1109/CVPR.2005.177
[85] L Ballerini, R B Fisher, B Aldridge, et al. A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions, color medical image analysis. Dordrecht:Springer, 2013.
[86] C Leo, V Bevilacqua, L Ballerini, et al. Hierarchical classification of ten skin lesion classes. Proc. SICSA Dundee Medical Image Analysis Workshop, 2015.
[87] K Shimizu, H Iyatomi, M E Celebi, et al. Four-class classification of skin lesions with task decomposition strategy. IEEE Transactions on Biomedical Engineering, 2015, 62(1):274-283.
[88] A Zaidan, B Zaidan, O Albahri, et al. A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking:Coherent taxonomy, open issues and recommendation pathway solution. Health and Technology, 2018:1-16.
[89] T-T Do, Y Zhou, H Zheng, et al. Early melanoma diagnosis with mobile imaging. Conf. Proc. IEEE Eng. Med. Biol. Soc., 2014:6752-6757, https://doi.org/10.1109/EMBC.2014.6945178
[90] A Masood, A Al-Jumaily, K Anam. Self-supervised learning model for skin cancer diagnosis. 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015:1012-1015, https://doi.org/10.1109/NER.2015.7146798
[91] M F Duarte, T E Matthews, W S Warren, et al. Melanoma classification from Hidden Markov tree features. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012:685-688, https://doi.org/10.1109/ICASSP.2012.6287976
[92] K Phillips, O Fosu, I Jouny. Mobile melanoma detection application for android smart phones. 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015:1-2, https://doi.org/10.1109/NEBEC.2015.7117184
[93] F Topfer, S Dudorov, J Oberhammer. Millimeter-wave near-field probe designed for high-resolution skin cancer diagnosis. IEEE Transactions on Microwave Theory & Techniques, 2015, 63(6):2050-2059.
[94] I Valavanis, K Moutselos, I Maglogiannis, et al. Inference of a robust diagnostic signature in the case of Melanoma:Gene selection by information gain and Gene Ontology tree exploration. 13th IEEE International Conference on BioInformatics and BioEngineering, 2013:1-4, https://doi.org/10.1109/BIBE.2013.6701618
[95] P Sabouri, H GholamHosseini, T Larsson, et al. A cascade classifier for diagnosis of melanoma in clinical images. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014:6748-6751, https://doi.org/10.1109/EMBC.2014.6945177
[96] M Efimenko, A Ignatev, K Koshechkin. Review of medical image recognition technologies to detect melanomas using neural networks. BMC Bioinformatics, 2020, 21(11):1-7.
[97] H L Semigran, D M Levine, S Nundy, et al. Comparison of physician and computer diagnostic accuracy. Jama Intern. Med., 2016, 176(12):1860-1861.
[98] C Ross, I Swetlitz. IBM's Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show, Stat News, 2018, https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments.
[99] D Castelvecchi. Can we open the black box of AI? Nature News, 2016, 538(7623):20.
[100] D Weinberger, Our machines now have knowledge we'll never understand, Backchannel, 2017, https://www.wired.com/story/our-machines-now-have-knowledge-well-never-understand.
[101] A Körner, R Garland, Z Czajkowska, et al. Supportive care needs and distress in patients with non-melanoma skin cancer:Nothing to worry about? European Journal of Oncology Nursing, 2016, 20:150-155.
[102] O Malyuskin, V Fusco. Resonance microwave reflectometry for early stage skin cancer identification. 2015 9th European Conference on Antennas and Propagation (EuCAP), 2015:1-6.
[103] S Serte, A Serener, F Al-Turjman. Deep learning in medical imaging:A brief review. Trans. Emerging Tel. Tech., 2020:e4080.
[104] C M Doran, R Ling, J Byrnes, et al. Benefit cost analysis of three skin cancer public education mass-media campaigns implemented in New South Wales, Australia. Plos One, 2016, 11(1):e0147665.
[105] A P Miller. Want less-biased decisions? Use algorithms. Harvard Business Review, 2018.
[106] Gautam, Diwakar, Ahmed, et al. Machine learning-based diagnosis of melanoma using macro images. International Journal for Numerical Methods in Biomedical Engineering, 2018, 34(5):e2953.1.
[107] W Fang, Y Li, H Zhang, et al. On the throughput-energy tradeoff for data transmission between cloud and mobile devices. Information Sciences, 2014, 283:79-93, https://doi.org/10.1016/j.ins.2014.06.022
[108] J He, S L Baxter, J Xu, et al. The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 2019, 25(1):30.
[109] E J Topol. High-performance medicine:the convergence of human and artificial intelligence. Nature Medicine, 2019, 25(1):44-56.