Nothing Special   »   [go: up one dir, main page]

Che et al., 2023 - Google Patents

Image quality-aware diagnosis via meta-knowledge co-embedding

Che et al., 2023

View PDF
Document ID
15399431415562055385
Author
Che H
Chen S
Chen H
Publication year
Publication venue
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

External Links

Snippet

Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models. To resolve this problem, most previous works have focused on filtering out degradation-causing low-quality images while …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

    • 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
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • 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
    • 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
    • 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
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • 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
    • 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
Li et al. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
US10430946B1 (en) Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques
Yu et al. Unest: local spatial representation learning with hierarchical transformer for efficient medical segmentation
Wang et al. A deep learning approach for meibomian gland atrophy evaluation in meibography images
George et al. Attention-guided 3D-CNN framework for glaucoma detection and structural-functional association using volumetric images
Che et al. Image quality-aware diagnosis via meta-knowledge co-embedding
Sander et al. Automatic segmentation with detection of local segmentation failures in cardiac MRI
Mangaokar et al. Jekyll: Attacking medical image diagnostics using deep generative models
Xie et al. End-to-end fovea localisation in colour fundus images with a hierarchical deep regression network
Shi et al. Translation of color fundus photography into fluorescein angiography using deep learning for enhanced diabetic retinopathy screening
Ryu et al. A deep learning algorithm for classifying diabetic retinopathy using optical coherence tomography angiography
Liu et al. Disentangled representation learning for OCTA vessel segmentation with limited training data
He et al. Joined: Prior guided multi-task learning for joint optic disc/cup segmentation and fovea detection
Yang et al. Deep hybrid convolutional neural network for segmentation of melanoma skin lesion
Fu et al. A retrospective comparison of deep learning to manual annotations for optic disc and optic cup segmentation in fundus photographs
Leandro et al. Oct-based deep-learning models for the identification of retinal key signs
Yuen et al. Deep-learning–based pre-diagnosis assessment module for retinal photographs: A multicenter study
Kim et al. Interpretable deep learning‐based hippocampal sclerosis classification
Chun et al. The present and future of artificial intelligence-based medical image in diabetes mellitus: focus on analytical methods and limitations of clinical use
Wang et al. Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Raghavan Attention guided grad-CAM: an improved explainable artificial intelligence model for infrared breast cancer detection
Jiang et al. Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks
Rasheed et al. DDLSNet: A novel deep learning-based system for grading funduscopic images for glaucomatous damage
Sengupta et al. Ophthalmic diagnosis and deep learning–a survey
Hemalakshmi et al. Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images