Multi-label learning model for improving retinal image classification in diabetic retinopathy
暂无分享,去创建一个
[1] Jiebo Luo,et al. Learning multi-label scene classification , 2004, Pattern Recognit..
[2] Shehzad Khalid,et al. Identification and classification of microaneurysms for early detection of diabetic retinopathy , 2013, Pattern Recognit..
[3] Zhi-Hua Zhou,et al. ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..
[4] K. Bretonnel Cohen,et al. A shared task involving multi-label classification of clinical free text , 2007, BioNLP@ACL.
[5] Anders Green,et al. Global Prevalence of Diabetes: Estimates for the Year 2000 and Projections for 2030 Response to Rathman and Giani , 2004 .
[6] Yiqin Wang,et al. Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine , 2013, Science China Information Sciences.
[7] Sungbin Lim,et al. Automatic classification of diabetic macular edema in digital fundus images , 2011, 2011 IEEE Colloquium on Humanities, Science and Engineering.
[8] Li Zhang,et al. An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).
[9] Fouad Khelifi,et al. Detection and classification of retinal fundus images exudates using region based multiscale LBP texture approach , 2016, 2016 International Conference on Control, Decision and Information Technologies (CoDIT).
[10] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[11] Josef Kittler,et al. Multilabel classification using heterogeneous ensemble of multi-label classifiers , 2012, Pattern Recognit. Lett..
[12] Yuanping Zhu,et al. Calibrated Rank-SVM for multi-label image categorization , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[13] Guozheng Li,et al. Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning , 2010, BMC complementary and alternative medicine.
[14] Min-Ling Zhang,et al. A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.
[15] Eyke Hüllermeier,et al. Combining Instance-Based Learning and Logistic Regression for Multilabel Classification , 2009, ECML/PKDD.
[16] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[17] Eyke Hüllermeier,et al. Dependent binary relevance models for multi-label classification , 2014, Pattern Recognit..
[18] Rui Guo,et al. Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis , 2012, Evidence-based complementary and alternative medicine : eCAM.
[19] Lixin Duan,et al. Multiple ocular diseases detection by graph regularized multi-label learning , 2014 .
[20] Shou-De Lin,et al. A Ranking-based KNN Approach for Multi-Label Classification , 2012, ACML.
[21] Fabio A. González,et al. Histopathology Image Classification Using Bag of Features and Kernel Functions , 2009, AIME.
[22] Josef Kittler,et al. Multi-label classification using stacked spectral kernel discriminant analysis , 2016, Neurocomputing.
[23] Lihteh Wu,et al. Classification of diabetic retinopathy and diabetic macular edema. , 2013, World journal of diabetes.
[24] Majid Mirmehdi,et al. Comparative Exudate Classification Using Support Vector Machines and Neural Networks , 2002, MICCAI.
[25] Jee-Hyong Lee,et al. An approach for multi-label classification by directed acyclic graph with label correlation maximization , 2016, Inf. Sci..
[26] Leandro dos Santos Coelho,et al. A RBF neural network model with GARCH errors: Application to electricity price forecasting , 2011 .
[27] Newton Spolaôr,et al. A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach , 2013, CLEI Selected Papers.
[28] Kenneth W. Tobin,et al. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..