[go: up one dir, main page]

Li et al., 2021 - Google Patents

SAP‐cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling

Li et al., 2021

Document ID
10073247372972817187
Author
Li Y
Zhao G
Zhang Q
Lin Y
Wang M
Publication year
Publication venue
Medical Physics

External Links

Snippet

Purpose Breast mass segmentation is a prerequisite step in the use of computer‐aided tools designed for breast cancer diagnosis and treatment planning. However, mass segmentation remains challenging due to the low contrast, irregular shapes, and fuzzy boundaries of …
Continue reading at aapm.onlinelibrary.wiley.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

Similar Documents

Publication Publication Date Title
Altaf et al. Going deep in medical image analysis: concepts, methods, challenges, and future directions
Mahmood et al. Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
Malhotra et al. [Retracted] Deep Neural Networks for Medical Image Segmentation
Zhang et al. Review of breast cancer pathologigcal image processing
Sahiner et al. Deep learning in medical imaging and radiation therapy
Soni et al. Light weighted healthcare CNN model to detect prostate cancer on multiparametric MRI
Liu et al. Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images
Wu et al. Classification of lung nodules based on deep residual networks and migration learning
Xing et al. Lesion segmentation in ultrasound using semi-pixel-wise cycle generative adversarial nets
Wang et al. Residual feedback network for breast lesion segmentation in ultrasound image
Ouyang et al. Rethinking U‐net from an attention perspective with transformers for osteosarcoma MRI image segmentation
Singh et al. An efficient hybrid methodology for an early detection of breast cancer in digital mammograms
Li et al. SAP‐cGAN: Adversarial learning for breast mass segmentation in digital mammogram based on superpixel average pooling
Xu et al. 3D‐SIFT‐Flow for atlas‐based CT liver image segmentation
Xu et al. Mammographic mass segmentation using multichannel and multiscale fully convolutional networks
Liang et al. Dense networks with relative location awareness for thorax disease identification
Haq An overview of deep learning in medical imaging
SS et al. Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review
Ahmad et al. Deep learning models for CT image classification: a comprehensive literature review
Delmoral et al. Semantic segmentation of CT liver structures: a systematic review of recent trends and bibliometric analysis: neural network-based methods for liver semantic segmentation
D'souza et al. SANAS-Net: spatial attention neural architecture search for breast cancer detection
Hariharan et al. Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models
Chen et al. A new classification method in ultrasound images of benign and malignant thyroid nodules based on transfer learning and deep convolutional neural network
Sudha et al. Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN
Han et al. Multitask network for thyroid nodule diagnosis based on TI‐RADS