Sarhan et al., 2019 - Google Patents
Multi-scale microaneurysms segmentation using embedding triplet lossSarhan et al., 2019
View PDF- Document ID
- 4998741206754565304
- Author
- Sarhan M
- Albarqouni S
- Yigitsoy M
- Navab N
- Eslami A
- Publication year
- Publication venue
- International conference on medical image computing and computer-assisted intervention
External Links
Snippet
Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms …
- 208000009857 Microaneurysm 0 title abstract description 39
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sarhan et al. | Multi-scale microaneurysms segmentation using embedding triplet loss | |
Khojasteh et al. | Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms | |
Beeche et al. | Super U-Net: A modularized generalizable architecture | |
Cheng et al. | Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features | |
Wang et al. | Automated pulmonary nodule detection: High sensitivity with few candidates | |
Li et al. | Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation | |
Jesson et al. | CASED: curriculum adaptive sampling for extreme data imbalance | |
Xiao et al. | Improving lesion segmentation for diabetic retinopathy using adversarial learning | |
Sun et al. | Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening | |
Garg et al. | A real time cloud-based framework for glaucoma screening using EfficientNet | |
Gamage et al. | Instance-based segmentation for boundary detection of neuropathic ulcers through Mask-RCNN | |
Wang et al. | Automatic classification of volumetric optical coherence tomography images via recurrent neural network | |
Fu et al. | MCLNet: An multidimensional convolutional lightweight network for gastric histopathology image classification | |
Wang et al. | A deep learning based pipeline for image grading of diabetic retinopathy | |
Gupta et al. | Brain tumor segmentation from MRI images using deep learning techniques | |
Zhai et al. | Retinal vessel image segmentation algorithm based on encoder-decoder structure | |
Lin et al. | Blu-gan: Bi-directional convlstm u-net with generative adversarial training for retinal vessel segmentation | |
Ali et al. | Lightweight encoder-decoder architecture for foot ulcer segmentation | |
Chak et al. | Neural network and svm based kidney stone based medical image classification | |
Sarhan et al. | Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations | |
He et al. | Skin lesion segmentation via deep RefineNet | |
Cheng et al. | Enhanced MobileNet for skin cancer image classification with fused spatial channel attention mechanism | |
Jain et al. | Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier | |
Gururaj et al. | Fundus image features extraction for exudate mining in coordination with content based image retrieval: a study | |
Garcia-Lamont et al. | Nucleus segmentation of white blood cells in blood smear images by modeling the pixels’ intensities as a set of three Gaussian distributions |