Guo et al., 2019 - Google Patents
L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus imagesGuo et al., 2019
- Document ID
- 1747884587742979753
- Author
- Guo S
- Li T
- Kang H
- Li N
- Zhang Y
- Wang K
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
Diabetic retinopathy and diabetic macular edema are the two leading causes for blindness in working-age people, and the quantitative and qualitative diagnosis of these two diseases usually depends on the presence and areas of lesions in fundus images. The main related …
- 230000011218 segmentation 0 title abstract description 118
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
- G06K9/62—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
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Guo et al. | L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images | |
Zhang et al. | Deep supervision with additional labels for retinal vessel segmentation task | |
Kassim et al. | Clustering-based dual deep learning architecture for detecting red blood cells in malaria diagnostic smears | |
Gecer et al. | Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks | |
Bilal et al. | A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images | |
Chagas et al. | Classification of glomerular hypercellularity using convolutional features and support vector machine | |
Dai et al. | Clinical report guided retinal microaneurysm detection with multi-sieving deep learning | |
Sopharak et al. | Machine learning approach to automatic exudate detection in retinal images from diabetic patients | |
Kou et al. | Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network | |
Bozorgtabar et al. | Skin lesion segmentation using deep convolution networks guided by local unsupervised learning | |
BenTaieb et al. | Predicting cancer with a recurrent visual attention model for histopathology images | |
Guo et al. | Bin loss for hard exudates segmentation in fundus images | |
Molina et al. | Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks | |
Huang et al. | Lesion-based contrastive learning for diabetic retinopathy grading from fundus images | |
Liz et al. | Deep learning for understanding multilabel imbalanced Chest X-ray datasets | |
Ouyang et al. | LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation | |
Yang et al. | RADCU-Net: Residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation | |
Banik et al. | A multi-scale patch-based deep learning system for polyp segmentation | |
Depto et al. | Automatic segmentation of blood cells from microscopic slides: a comparative analysis | |
Abdulaal et al. | A self-learning deep neural network for classification of breast histopathological images | |
Singh et al. | Deep attention network for pneumonia detection using chest X-ray images | |
Elaouaber et al. | Blood vessel segmentation using deep learning architectures for aid diagnosis of diabetic retinopathy | |
Raja Kumar et al. | Detection of diabetic retinopathy using deep convolutional neural networks | |
Valdez et al. | Medicinal plant classification using convolutional neural network and transfer learning | |
Fu et al. | Automatic grading of Diabetic macular edema based on end-to-end network |