Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks
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Muhammad Naseer Bajwa | Kaoru Muta | Muhammad Imran Malik | Shoaib Ahmed Siddiqui | Stephan Alexander Braun | Bernhard Homey | Andreas Dengel | Sheraz Ahmed | A. Dengel | M. I. Malik | Sheraz Ahmed | B. Homey | S. Braun | Kaoru Muta
[1] A. Tanaka,et al. Identification of Marek's disease virus nuclear antigen in latently infected lymphoblastoid cells , 1988, Journal of virology.
[2] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[3] Zhuowen Tu,et al. Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree , 2015, AISTATS.
[4] Marcel F. Jonkman,et al. MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images , 2015, Expert Syst. Appl..
[5] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[7] Woohyung Lim,et al. Interpretation of the Outputs of a Deep Learning Model Trained with a Skin Cancer Dataset. , 2018, The Journal of investigative dermatology.
[8] Eduardo Valle,et al. Knowledge transfer for melanoma screening with deep learning , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[9] David Sontag,et al. Prototypical Clustering Networks for Dermatological Disease Diagnosis , 2018, ArXiv.
[10] Hao Chen,et al. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.
[11] Mohammad H. Jafari,et al. Melanoma detection by analysis of clinical images using convolutional neural network , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[12] Abder-Rahman Ali,et al. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data , 2012, Medical Imaging.
[13] John R. Smith,et al. Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images , 2015, MLMI.
[14] H. Haenssle,et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.
[15] João Manuel R. S. Tavares,et al. Effective features to classify skin lesions in dermoscopic images , 2017, Expert Syst. Appl..
[16] Eyal Toledano,et al. Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement , 2017, Journal of Digital Imaging.
[17] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[18] Rahil Garnavi,et al. Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[19] LinLin Shen,et al. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.
[20] Noel C. F. Codella,et al. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.
[21] Sherin M. Youssef,et al. Computer-Aided Model for Skin Diagnosis Using Deep Learning , 2016 .
[22] Achim Hekler,et al. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review , 2018, Journal of medical Internet research.
[23] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Richard D. White,et al. Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging. , 2017, Radiology.
[25] Pedro M. Ferreira,et al. PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[26] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] A. Blum,et al. Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology , 2004, The British journal of dermatology.
[28] Noel C. F. Codella,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) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[29] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[30] Sebastian Thrun,et al. Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning , 2016, AAAI Workshops.
[31] Haofu Liao,et al. A Deep Learning Approach to Universal Skin Disease Classification , 2015 .
[32] Ghassan Hamarneh,et al. Deep features to classify skin lesions , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[33] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[34] Muhammad Naseer Bajwa,et al. Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection , 2019, MIUA.
[35] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] Xavier Giro-i-Nieto,et al. Skin lesion classification from dermoscopic images using deep learning techniques , 2017, 2017 13th IASTED International Conference on Biomedical Engineering (BioMed).
[37] Kensuke Yokoi,et al. APAC: Augmented PAttern Classification with Neural Networks , 2015, ArXiv.
[38] Benjamin Graham,et al. Fractional Max-Pooling , 2014, ArXiv.
[39] Andrés Montoyo,et al. Advances on natural language processing , 2007, Data Knowl. Eng..
[40] S. Han,et al. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. , 2018, The Journal of investigative dermatology.
[41] Glenda Michele Botelho,et al. Deep Learning and Convolutional Neural Networks in the Aid of the Classification of Melanoma , 2016 .
[42] Robert B. Fisher,et al. A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions , 2013 .
[43] Giovanni Pellacani,et al. Validity and Reliability of Dermoscopic Criteria Used to Differentiate Nevi From Melanoma: A Web-Based International Dermoscopy Society Study. , 2016, JAMA dermatology.
[44] Cristian Navarrete-Dechent,et al. Automated Dermatological Diagnosis: Hype or Reality? , 2018, The Journal of investigative dermatology.
[45] Giuseppe Argenziano,et al. Interactive Atlas of Dermoscopy (Book and CD-ROM) , 2000 .
[46] A Lijiya,et al. Skin Lesion Analysis Towards Melanoma Detection , 2019, 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).
[47] Andreas Dengel,et al. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning , 2019, BMC Medical Informatics and Decision Making.
[48] Ammara Masood,et al. Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms , 2013, Int. J. Biomed. Imaging.