Classification of Lentigo Maligna at Patient-Level by Means of Reflectance Confocal Microscopy Data
暂无分享,去创建一个
Franck Marzani | Romain Cendre | Alamin Mansouri | Jean-Luc Perrot | Elisa Cinotti | A. Mansouri | E. Cinotti | J. Perrot | F. Marzani | R. Cendre
[1] Masaru Tanaka,et al. Classification of melanocytic skin lesions from non-melanocytic lesions , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[2] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[3] M. Lupu,et al. A Systematic Review and Meta-Analysis of the Accuracy of in Vivo Reflectance Confocal Microscopy for the Diagnosis of Primary Basal Cell Carcinoma , 2019, Journal of clinical medicine.
[4] Jean-Paul Gauthier,et al. Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context , 2007, Journal of Mathematical Imaging and Vision.
[5] B. Rao,et al. Using Reflectance Confocal Microscopy in Skin Cancer Diagnosis. , 2017, Dermatologic clinics.
[6] James R. Foulds,et al. A review of multi-instance learning assumptions , 2010, The Knowledge Engineering Review.
[7] M Wiltgen,et al. Automatic Identification of Diagnostic Significant Regions in Confocal Laser Scanning Microscopy of Melanocytic Skin Tumors , 2008, Methods of Information in Medicine.
[8] H. Soyer,et al. Evidence-Based Clinical Practice Guidelines for the Management of Patients with Lentigo Maligna , 2019, Dermatology.
[9] Silvio Borer,et al. Normalization in Support Vector Machines , 2001, DAGM-Symposium.
[10] Y. Benezeth,et al. Two Schemes for Automated Diagnosis of Lentigo on Confocal Microscopy Images , 2019, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).
[11] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[12] John Paoli,et al. Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin , 2017, Journal of the American Academy of Dermatology.
[13] Samuel C Hames,et al. Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks , 2016, PloS one.
[14] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[16] Aaron S Farberg,et al. The Importance of Early Recognition of Skin Cancer. , 2017, Dermatologic clinics.
[17] R. Braun,et al. How Reflectance Confocal Microscopy Works , 2012 .
[18] Franck Marzani,et al. Automatic differentiation of melanoma from dysplastic nevi , 2015, Comput. Medical Imaging Graph..
[19] A. Rossi,et al. Lentigo maligna melanoma mapping using reflectance confocal microscopy correlates with staged excision: A prospective study. , 2019, Journal of the American Academy of Dermatology.
[20] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[21] Gavin C. Cawley,et al. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..
[22] A. Cesinaro,et al. Impact of Dermoscopy and Reflectance Confocal Microscopy on the Histopathologic Diagnosis of Lentigo Maligna/Lentigo Maligna Melanoma , 2018, The American Journal of dermatopathology.
[23] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[24] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[25] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] P. C. Siddalingaswamy,et al. Techniques and algorithms for computer aided diagnosis of pigmented skin lesions - A review , 2018, Biomed. Signal Process. Control..
[27] Jennifer G. Dy,et al. A machine learning method for identifying morphological patterns in reflectance confocal microscopy mosaics of melanocytic skin lesions in-vivo , 2016, SPIE BiOS.
[28] M. Čeh,et al. CeO2 thin films obtained by sol–gel deposition and annealed in air or argon , 2002 .
[29] Milind Rajadhyaksha,et al. Smartphone confocal microscopy for imaging cellular structures in human skin in vivo. , 2018, Biomedical optics express.
[30] Hadj Batatia,et al. Wavelet-based statistical classification of skin images acquired with reflectance confocal microscopy. , 2017, Biomedical optics express.
[31] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[32] Luis Pedro Coelho,et al. Mahotas: Open source software for scriptable computer vision , 2012, ArXiv.
[33] Cheng-Xin Li,et al. A meta‐analysis comparing confocal microscopy and dermoscopy in diagnostic accuracy of lentigo maligna , 2019, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.
[34] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[35] Gabriela Oana Cula,et al. Automatic Localization of Skin Layers in Reflectance Confocal Microscopy , 2014, ICIAR.
[36] Giovanni Pellacani,et al. Automated detection of malignant features in confocal microscopy on superficial spreading melanoma versus nevi. , 2010, Journal of biomedical optics.
[37] Milind Rajadhyaksha,et al. Skin imaging with reflectance confocal microscopy. , 2008, Seminars in cutaneous medicine and surgery.
[38] Gary Doran,et al. A theoretical and empirical analysis of support vector machine methods for multiple-instance classification , 2014, Machine Learning.
[39] R. Hofmann-Wellenhof,et al. Dermoscopy vs. reflectance confocal microscopy for the diagnosis of lentigo maligna , 2018, Journal of the European Academy of Dermatology and Venereology : JEADV.