[go: up one dir, main page]

Kensert et al., 2019 - Google Patents

Transfer learning with deep convolutional neural networks for classifying cellular morphological changes

Kensert et al., 2019

View HTML @Full View
Document ID
13104738013265945172
Author
Kensert A
Harrison P
Spjuth O
Publication year
Publication venue
SLAS Discovery: Advancing Life Sciences R&D

External Links

Snippet

The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis …
Continue reading at journals.sagepub.com (HTML) (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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • 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
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • 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
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • 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
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00Record carriers for use with machines and with at least a part designed to carry digital markings
    • 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
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services

Similar Documents

Publication Publication Date Title
Kensert et al. Transfer learning with deep convolutional neural networks for classifying cellular morphological changes
Mishra et al. Convolutional neural network for histopathological analysis of osteosarcoma
Ljosa et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment
Kandaswamy et al. High-content analysis of breast cancer using single-cell deep transfer learning
Janssens et al. Fully unsupervised deep mode of action learning for phenotyping high-content cellular images
Hu et al. Classification of metaphase chromosomes using deep convolutional neural network
Horvath et al. Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results
Abraham et al. Multiparametric analysis of screening data: growing beyond the single dimension to infinity and beyond
Wang et al. A systematic evaluation of computational methods for cell segmentation
Dürr et al. Know when you don't know: a robust deep learning approach in the presence of unknown phenotypes
Muniasamy et al. Lung cancer histopathology image classification using transfer learning with convolution neural network model
Vidyarthi et al. Texture based feature extraction method for classification of brain tumor MRI
Zhang et al. Out-of-sample data visualization using bi-kernel t-SNE
Rahman et al. ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data
Kümmel et al. Comparison of multivariate data analysis strategies for high-content screening
Akash et al. CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities
Qian et al. MSNSegNet: attention-based multi-shape nuclei instance segmentation in histopathology images
Tucci et al. The digital revolution in pathology: Towards a smarter approach to research and treatment
Wei et al. Enhancing risk management in inclusive ESG investment portfolios in financial markets through big data analysis
Zhao et al. A spectral-spatial SVM-based multi-layer learning algorithm for hyperspectral image classification
Xu et al. The image segmentation algorithm of colorimetric sensor array based on fuzzy C-means clustering
Janosch et al. Unbiased phenotype detection using negative controls
Wu et al. Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization
Wu et al. Factor knowledge mining using the techniques of ai neural networks and self-organizing map
Kolla et al. Breast cancer diagnosis through knowledge distillation of Swin transformer-based teacher–student models