[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wang et al., 2014 - Google Patents

Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

Wang et al., 2014

View HTML
Document ID
4949390313871306704
Author
Wang H
Cruz-Roa A
Basavanhally A
Gilmore H
Shih N
Feldman M
Tomaszewski J
Gonzalez F
Madabhushi A
Publication year
Publication venue
Journal of Medical Imaging

External Links

Snippet

Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (ie, undergoing mitosis) at a …
Continue reading at www.ncbi.nlm.nih.gov (HTML) (other versions)

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
    • G06K9/62—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
    • 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
    • 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
    • G06T2207/30—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
    • 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
    • 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
    • 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
    • G06Q50/10—Services
    • 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
    • G06Q10/00—Administration; Management
    • 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
Wang et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features
Wang et al. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection
Hamidian et al. 3D convolutional neural network for automatic detection of lung nodules in chest CT
Jnawali et al. Deep 3D convolution neural network for CT brain hemorrhage classification
Nirschl et al. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue
Cruz-Roa et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks
Helwan et al. Deep networks in identifying CT brain hemorrhage
Janowczyk et al. A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images
Kou et al. Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network
Kumar et al. Convolutional neural networks for prostate cancer recurrence prediction
Khan et al. Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM
Chudzik et al. Exudate segmentation using fully convolutional neural networks and inception modules
Li et al. Lung nodule malignancy prediction using multi-task convolutional neural network
Ahmad et al. Convolutional-neural-network-based feature extraction for liver segmentation from CT images
Remedios et al. Classifying magnetic resonance image modalities with convolutional neural networks
Barbu et al. An analysis of robust cost functions for CNN in computer-aided diagnosis
Tarando et al. Increasing CAD system efficacy for lung texture analysis using a convolutional network
Han et al. Nuclei counting in microscopy images with three dimensional generative adversarial networks
Yang et al. Cascading yolo: automated malaria parasite detection for plasmodium vivax in thin blood smears
Qayyum et al. Efficient multi-model vision transformer based on feature fusion for classification of dfuc2021 challenge
Han et al. Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens
Wang et al. Boundary determination of foot ulcer images by applying the associative hierarchical random field framework
Wang et al. A hybrid deep learning approach to predict malignancy of breast lesions using mammograms
Fu et al. A deep convolutional feature based learning layer-specific edges method for segmenting OCT image
Corredor et al. Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features