Zhang et al., 2021 - Google Patents
Review of breast cancer pathologigcal image processingZhang et al., 2021
View PDF- Document ID
- 7970355502960645575
- Author
- Zhang Y
- Xia K
- Li C
- Wei B
- Zhang B
- Publication year
- Publication venue
- BioMed research international
External Links
Snippet
Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has …
- 206010006187 Breast cancer 0 title abstract description 66
Classifications
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- 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
- 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
- G06F19/32—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
- 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
- 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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Review of breast cancer pathologigcal image processing | |
Men et al. | Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks | |
Frid-Adar et al. | GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification | |
Al-Antari et al. | A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification | |
Mall et al. | A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities | |
Cheng et al. | Contour-aware semantic segmentation network with spatial attention mechanism for medical image | |
Zhong et al. | Boosting‐based cascaded convolutional neural networks for the segmentation of CT organs‐at‐risk in nasopharyngeal carcinoma | |
Abbasi et al. | Medical image registration using unsupervised deep neural network: A scoping literature review | |
Göçeri | Fully automated liver segmentation using Sobolev gradient‐based level set evolution | |
Fei et al. | Medical image fusion based on feature extraction and sparse representation | |
Xue et al. | Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images | |
Gu et al. | Segmentation of coronary arteries images using global feature embedded network with active contour loss | |
Nizamani et al. | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data | |
Liang et al. | Residual convolutional neural networks with global and local pathways for classification of focal liver lesions | |
Tummala et al. | Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network | |
Shao et al. | Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review. | |
Wu et al. | Automatic semicircular canal segmentation of CT volumes using improved 3D U‐Net with attention mechanism | |
Li et al. | Medical image identification methods: a review | |
Dong et al. | An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs | |
Yang et al. | Multi-modality relation attention network for breast tumor classification | |
Murmu et al. | A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor | |
Xia et al. | Cross-domain brain CT image smart segmentation via shared hidden space transfer FCM clustering | |
Shen | [Retracted] Implementation of CT Image Segmentation Based on an Image Segmentation Algorithm | |
Li et al. | SAP‐cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling | |
Xiao et al. | PET and CT image fusion of lung cancer with siamese pyramid fusion network |