[go: up one dir, main page]

Hradel et al., 2020 - Google Patents

Interpretable diagnosis of breast cancer from histological images using Siamese neural networks

Hradel et al., 2020

Document ID
2293951572643084261
Author
Hradel D
Hudec L
Benesova W
Publication year
Publication venue
Twelfth International Conference on Machine Vision (ICMV 2019)

External Links

Snippet

Breast cancer is one of the most widespread causes of women's death worldwide. Successful treatment can be achieved only by the early and accurate tumor diagnosis. The main method of tissue diagnosis taken by biopsy is based on the observation of its …
Continue reading at www.spiedigitallibrary.org (other versions)

Classifications

    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • 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
    • 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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228Selecting the most significant subset of features
    • 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
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/00147Matching; Classification
    • 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
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014Pre-processing, e.g. image segmentation ; Feature extraction
    • 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
    • G06T2207/30024Cell structures in vitro; Tissue sections in vitro
    • 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
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • 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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
KR102108050B1 (en) Method for classifying breast cancer histology images through incremental boosting convolution networks and apparatus thereof
Kowal et al. Breast cancer nuclei segmentation and classification based on a deep learning approach
JP2017519985A (en) Digital holographic microscopy data analysis for hematology
Irshad et al. Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology
Ström et al. Pathologist-level grading of prostate biopsies with artificial intelligence
Bouatmane et al. Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery
AU2021349226C1 (en) Critical component detection using deep learning and attention
KR20230063147A (en) Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis Method and System
Kanwal et al. Quantifying the effect of color processing on blood and damaged tissue detection in whole slide images
Nobile et al. Unsupervised neural networks as a support tool for pathology diagnosis in MALDI-MSI experiments: A case study on thyroid biopsies
Alisha et al. Cervical cell nuclei segmentation on pap smear images using deep learning technique
Altuntaş et al. Categorization of breast carcinoma histopathology images by utilizing region-based convolutional neural networks
Carvalho et al. Analysis of features for breast cancer recognition in different magnifications of histopathological images
Johny et al. Optimization of CNN model with hyper parameter tuning for enhancing sturdiness in classification of histopathological images
Tahat et al. Computer aided diagnosis of melanoma based on the abcd rule
Aziz et al. Deep learning approach for renal cell carcinoma detection, subtyping, and grading
Chen et al. What can machine vision do for lymphatic histopathology image analysis: a comprehensive review
Hradel et al. Interpretable diagnosis of breast cancer from histological images using Siamese neural networks
Zulfqar et al. Breast cancer diagnosis: A transfer learning-based system for detection of invasive ductal carcinoma (IDC)
Łowicki et al. Towards sustainable health-detection of tumor changes in breast histopathological images using deep learning
Mengoni et al. CAD Tool for Breast Cancer Prediction Using Multiple Deep-learning Models
Das et al. A texture based approach for automatic identification of benign and malignant tumor from FNAC images
Jayaraj Digital Pathology with Deep Learning for Diagnosis of Breast Cancer in Low-Resource Settings
Ehteshami Bejnordi Histopathological diagnosis of breast cancer using machine learning
Medeiros et al. Texture analysis based on structural co-occurrence matrix improves the colorectal tissue characterization