OCT影像分類1:相關論文統計
檔名 | 論文名 | 頁數 | 引用數 | 年限 | 期刊 | 期刊概述 | 方法概述 |
11 | Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections |
4 | 4 | 2016 | 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | 對OCT圖片分類,判斷是否患有糖尿性黃斑水腫; | |
12_nihms-508161 | Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography |
18 | 38 | 2013 | Ophthalmology | 利用4種疾病指標將OCT影像分類為AMD或正常: 包括視網膜總體積、色素上皮-德魯森複合體體積、異常色素上皮-德魯森複合體體積和變薄體積。 | |
13_3568 | Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images |
10 | 23 | 2014 | Biomedical Optics Express | 以定向梯度解算器的多尺度直方圖作為支援向量機的特徵向量進行分類,OCT影像分為三類:AMD、DME或Normal | |
14_nihms307611 | Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding | 36 | 47 | 2011 | Medical Image Analysis | 採用機器學習的方法,利用紋理和形狀編碼的多尺度空間金字塔和區域性二值模式,自動檢測視網膜OCT影像中的黃斑病變。 | |
15_deepmind論文眼底圖分類 | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs | 深度學習分類眼底圖 | |||||
16_1-s2.0-S2468653016301749-main | Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images |
6 | 10 | 2016 | Ophthalmology Retina | ||
17_07493240 | Detection of age-related macular degeneration via deep learning | 5 | 8 | 2016 | IEEE International Symposium on Biomedical Imaging | ||
18_579 | Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration |
14 | 3 | 2017 | Biomedical Optics Express | 3.482,SCI三區 | |
19_i1552-5783-57-13-5200 | Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning |
7 | 20 | 2016 | Investigative Opthalmology & Visual Science | SCI,3.303 | 深度學習分類眼底圖 |
12938_2017_Article_352 | Machine learning techniques for diabetic macular edema (DME) classification on SD‑OCT images |
12 | 1 | 2017 | BioMedical Engineering OnLine | ||
6556778 | SVM-BASED TEXTURE CLASSIFICATION IN OPTICAL COHERENCE TOMOGRAPHY | 4 | 15 | 2013/4/1 | Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on | ||
7017441 | Automated Classification between Age-related Macular Degeneration and Diabetic Macular Edema in OCT Image Using Image Segmentation |
4 | 2014 | The 2014 Biomedical Engineering International Conference (BMEiCON-2014) | |||
54823078 | Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach |
7 | 5 | 2016/3/24 | Medical Imaging 2016: Computer-Aided Diagnosis | ||
54824048 | Classification of SD-OCT Volumes using Local Binary Patterns: Experimental Validation for DME Detection |
28 | 4 | 2016 | Journal of Ophthalmology | ||
Alsaih2017_Article_MachineLearningTechniquesForDi | Machine learning techniques for diabetic macular edema (DME) classification on SD‑OCT images |
12 | 1 | 2017 | BioMedical Engineering OnLine | ||
Baroni2007_Chapter_TextureClassificationOfRetinal | Texture Classification of Retinal Layers in Optical Coherence Tomography | 4 | 21 | 2007 | |||
journal.pone.0198281 | Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm |
17 | 2017 | PLOS ONE | SCI3區,2.76 |
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