Multi-class segmentation of skin lesions via joint dictionary learning
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[1] S. Menzies,et al. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting , 2008, The British journal of dermatology.
[2] R. Moy. Clinical presentation of actinic keratoses and squamous cell carcinoma. , 2000, Journal of the American Academy of Dermatology.
[3] René Vidal,et al. Sparse Dictionaries for Semantic Segmentation , 2014, ECCV.
[4] Dagan Feng,et al. Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks , 2017, IEEE Transactions on Biomedical Engineering.
[5] R. Joe Stanley,et al. Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images , 2011, Comput. Medical Imaging Graph..
[6] Michael Elad,et al. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.
[7] Jacob Scharcanski,et al. Automated prescreening of pigmented skin lesions using standard cameras , 2011, Comput. Medical Imaging Graph..
[8] Georg Langs,et al. The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification , 2020, Comput. Methods Programs Biomed..
[9] Massimo Tistarelli,et al. Feature Level Fusion of Face and Fingerprint Biometrics , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.
[10] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Jacob Scharcanski,et al. Segmentation of pigmented skin lesions using Non-negative Matrix Factorization , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.
[13] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Jun Gao,et al. A Multiview Joint Sparse Representation with Discriminative Dictionary for Melanoma Detection , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[15] LinLin Shen,et al. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.
[16] Masafumi Hagiwara,et al. Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system , 2006, Melanoma research.
[17] Mutlu Mete,et al. Lesion detection in demoscopy images with novel density-based and active contour approaches , 2010, BMC Bioinformatics.
[18] Shuicheng Yan,et al. Multi-task low-rank affinity pursuit for image segmentation , 2011, 2011 International Conference on Computer Vision.
[19] Rahil Garnavi,et al. Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement , 2016, MLMI@MICCAI.
[20] Masafumi Hagiwara,et al. An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm , 2008, Comput. Medical Imaging Graph..
[21] Enes Ayan,et al. Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm , 2019, Diagnostics.
[22] Niloofar Gheissari,et al. Segmentation of Dermoscopy Images Using Wavelet Networks , 2013, IEEE Transactions on Biomedical Engineering.
[23] Nezam Mahdavi-Amiri,et al. Kernel sparse representation based model for skin lesions segmentation and classification , 2019, Comput. Methods Programs Biomed..
[24] Y. C. Pati,et al. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.
[25] Edoardo Amaldi,et al. On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..
[26] James Llinas,et al. An introduction to multisensor data fusion , 1997, Proc. IEEE.
[27] Pramod K. Varshney. Multisensor data fusion , 1997 .
[28] R. Johr. Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. , 2002, Clinics in dermatology.
[29] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[31] M. Emin Yüksel,et al. Accurate Segmentation of Dermoscopic Images by Image Thresholding Based on Type-2 Fuzzy Logic , 2009, IEEE Transactions on Fuzzy Systems.
[32] Huiyu Zhou,et al. A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images , 2015 .
[33] M. Thissen,et al. Actinic keratosis: how to differentiate the good from the bad ones? , 2006, European journal of dermatology : EJD.
[34] M. Emre Celebi,et al. Dermoscopy Image Analysis: Overview and Future Directions , 2019, IEEE Journal of Biomedical and Health Informatics.
[35] Miguel R. D. Rodrigues,et al. Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries , 2017, IEEE Transactions on Computational Imaging.
[36] Rita Cucchiara,et al. Exploiting color and topological features for region segmentation with recursive fuzzy C-means , 2002 .
[37] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[38] Gerald Schaefer,et al. Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods , 2013, 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.
[39] Yading Yuan,et al. Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance , 2017, IEEE Transactions on Medical Imaging.
[40] J. Mayer,et al. Systematic review of the diagnostic accuracy of dermatoscopy in detecting malignant melanoma , 1997, The Medical journal of Australia.
[41] Franck Marzani,et al. Classification of melanoma lesions using sparse coded features and random forests , 2016, SPIE Medical Imaging.
[42] Nenghai Yu,et al. A multi-task framework with feature passing module for skin lesion classification and segmentation , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[43] Qaisar Abbas,et al. Unified approach for lesion border detection based on mixture modeling and local entropy thresholding , 2013, 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.
[44] Gerald Schaefer,et al. Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.
[45] David Zhang,et al. Relaxed collaborative representation for pattern classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Qaisar Abbas,et al. Skin tumor area extraction using an improved dynamic programming approach , 2012, 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.
[47] Michael Elad,et al. Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.
[48] Andreas E. Savakis,et al. LGE-KSVD: Robust Sparse Representation Classification , 2014, IEEE Transactions on Image Processing.
[49] Jacob Scharcanski,et al. Segmentation of Pigmented Melanocytic Skin Lesions Based on Learned Dictionaries and Normalized Graph Cuts , 2014, 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images.
[50] Alan C. Bovik,et al. Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm , 2013, Pattern Recognit..
[51] A. Jerant,et al. Early detection and treatment of skin cancer. , 2000, American family physician.
[52] John R. Smith,et al. Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images , 2015, MLMI.
[53] W. Stoecker,et al. Unsupervised border detection in dermoscopy images , 2007, 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.
[54] Arun Ross,et al. Feature level fusion of hand and face biometrics , 2005, SPIE Defense + Commercial Sensing.
[55] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[56] Michael Elad,et al. Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..
[57] Gerald Schaefer,et al. Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..
[58] Hao Chen,et al. Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.
[59] Fengying Xie,et al. Automatic Skin Lesion Segmentation Based on Texture Analysis and Supervised Learning , 2012, ACCV.
[60] S. Florell,et al. Lentigo Maligna/Lentigo Maligna Melanoma: Current State of Diagnosis and Treatment , 2006, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].
[61] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[62] Asok Ray,et al. Multimodal Task-Driven Dictionary Learning for Image Classification , 2015, IEEE Transactions on Image Processing.
[63] Thomas S. Huang,et al. Joint-Structured-Sparsity-Based Classification for Multiple-Measurement Transient Acoustic Signals , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[64] 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).
[65] Xuelong Li,et al. Mean shift based gradient vector flow for image segmentation , 2013, Comput. Vis. Image Underst..
[66] Kjersti Engan,et al. Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[67] Randy H. Moss,et al. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes , 2005, 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.
[68] Rama Chellappa,et al. In-Plane Rotation and Scale Invariant Clustering Using Dictionaries , 2013, IEEE Transactions on Image Processing.
[69] M. Tucker,et al. Dysplastic Nevi and Melanoma , 2013, Cancer Epidemiology, Biomarkers & Prevention.
[70] Anton Osokin,et al. Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[71] Constantine Butakoff,et al. Independent Histogram Pursuit for Segmentation of Skin Lesions , 2008, IEEE Transactions on Biomedical Engineering.
[72] David A. Clausi,et al. Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness , 2014, IEEE Transactions on Biomedical Engineering.