[go: up one dir, main page]

Ikeda et al., 2023 - Google Patents

Relationship between a deep learning model and liquid‐based cytological processing techniques

Ikeda et al., 2023

Document ID
12085659170483368283
Author
Ikeda K
Sakabe N
Maruyama S
Ito C
Shimoyama Y
Oboshi W
Komene T
Yamaguchi Y
Sato S
Nagata K
Publication year
Publication venue
Cytopathology

External Links

Snippet

Objective Artificial intelligence (AI)–based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid‐based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10Investigating individual particles
    • G01N15/14Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00Sampling; Preparing specimens for investigation
    • G01N1/28Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/30Staining; Impregnating Fixation; Dehydration; Multistep processes for preparing samples of tissue, cell or nucleic acid material and the like for analysis
    • 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
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N2015/0065Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials biological, e.g. blood
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • G01N27/26Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/416Systems

Similar Documents

Publication Publication Date Title
Courtiol et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome
Zhu et al. Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
Bertram et al. A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor
Cheng et al. Robust whole slide image analysis for cervical cancer screening using deep learning
Teramoto et al. Automated classification of lung cancer types from cytological images using deep convolutional neural networks
Chandradevan et al. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells
Miyoshi et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma
Bychkov et al. Deep learning based tissue analysis predicts outcome in colorectal cancer
Aubreville et al. A comprehensive multi-domain dataset for mitotic figure detection
Tayebi et al. Automated bone marrow cytology using deep learning to generate a histogram of cell types
Manescu et al. Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning
Lin et al. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology
Yazgan et al. Hürthle cell presence alters the distribution and outcome of categories in the Bethesda system for reporting thyroid cytopathology
Khutlang et al. Automated detection of tuberculosis in Ziehl‐Neelsen‐stained sputum smears using two one‐class classifiers
Idrees et al. A machine‐learning algorithm for the reliable identification of oral lichen planus
Li et al. A deep learning model for detection of leukocytes under various interference factors
Lv et al. High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system
Levy et al. Large‐scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis‐X
Ikeda et al. Relationship between a deep learning model and liquid‐based cytological processing techniques
Amaral et al. Classification and immunohistochemical scoring of breast tissue microarray spots
Ikeda et al. Characterizing the effect of processing technique and solution type on cytomorphology using liquid-based cytology
Ikeda et al. Relationship between liquid-based cytology preservative solutions and artificial intelligence: liquid-based cytology specimen cell detection using YOLOv5 deep convolutional neural network
Levy et al. Uncovering additional predictors of urothelial carcinoma from voided urothelial cell clusters through a deep learning–based image preprocessing technique
Barroeta et al. Utility of the Thin Prep Imaging System® in the detection of squamous intraepithelial abnormalities on retrospective evaluation: Can we trust the imager?
Wang et al. Application of the International System for Reporting Serous Fluid Cytopathology to pericardial fluid: root cause analysis of indeterminate diagnoses, cytohistological correlation, and assessment of malignancy risk