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

Wang et al., 2022 - Google Patents

Deep learning using endobronchial-ultrasound-guided transbronchial needle aspiration image to improve the overall diagnostic yield of sampling mediastinal …

Wang et al., 2022

View HTML
Document ID
3952439701706642297
Author
Wang C
Khalil M
Lin Y
Lee Y
Huang T
Chao T
Publication year
Publication venue
Diagnostics

External Links

Snippet

Lung cancer is the biggest cause of cancer-related death worldwide. An accurate nodal staging is critical for the determination of treatment strategy for lung cancer patients. Endobronchial-ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • 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
    • 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
    • G06Q50/22Health care, e.g. hospitals; Social work
    • 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
    • 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
    • G06F19/36Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
    • 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
    • G06F19/28Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • 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
    • G06Q50/01Social networking
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • 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
    • G01N1/00Sampling; Preparing specimens for investigation
    • 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

Similar Documents

Publication Publication Date Title
Lin et al. Deep learning fast screening approach on cytological whole slides for thyroid cancer diagnosis
Ahmad Fauzi et al. Allred scoring of ER-IHC stained whole-slide images for hormone receptor status in breast carcinoma
Chandramouli et al. Computer extracted features from initial H&E tissue biopsies predict disease progression for prostate cancer patients on active surveillance
Wang et al. Deep learning using endobronchial-ultrasound-guided transbronchial needle aspiration image to improve the overall diagnostic yield of sampling mediastinal lymphadenopathy
Hanis et al. Diagnostic accuracy of machine learning models on mammography in breast cancer classification: a meta-analysis
Faria et al. A novel convolutional neural network algorithm for histopathological lung cancer detection
Abdullahi Sidi et al. PD-L1 multiplex and quantitative image analysis for molecular diagnostics
Tavoraitė et al. Ultrasound assessment of adnexal pathology: standardized methods and different levels of experience
Tommola et al. The contributory role of cell blocks in salivary gland neoplasms fine needle aspirations classified by the Milan System for Reporting Salivary Gland Cytology
Committeri et al. Support tools in the differential diagnosis of salivary gland tumors through inflammatory biomarkers and radiomics metrics: a preliminary study
Žemaitis et al. Diagnostic yield of endobronchial ultrasound-guided transbronchial needle aspiration cytological smears and cell blocks: a single-institution experience
Osman et al. Classification of monocytes, promonocytes and monoblasts using deep neural network models: an area of unmet need in diagnostic hematopathology
Ren et al. Deep learning-based classification and targeted gene alteration prediction from pleural effusion cell block whole-slide images
Yu et al. A deep-learning-based artificial intelligence system for the pathology diagnosis of uterine smooth muscle tumor
Jia et al. Deep learning with transformer or convolutional neural network in the assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer based on US images: a dual-center retrospective study
Alqahtani Systematic review of AI-assisted MRI in prostate cancer diagnosis: enhancing accuracy through second opinion tools
Qin et al. Artificial intelligence in endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) for solid pancreatic lesions: Opportunities and challenges
Sun et al. Establishment of surgical difficulty grading system and application of MRI-based artificial intelligence to stratify difficulty in laparoscopic rectal surgery
Young et al. Predictors of invasiveness in adenocarcinoma of lung with lepidic growth pattern
Butureanu et al. Ovarian masses-applicable IOTA ADNEX model versus morphological findings for accurate diagnosis and treatment
Sbeit et al. The yield of string sign in differentiating mucinous from non-mucinous pancreatic cysts: A retrospective cross-sectional study
Mohamed et al. Endoscopic ultrasound-guided fine-needle biopsy versus aspiration for tissue sampling adequacy for molecular testing in pancreatic ductal adenocarcinoma
Turtoi et al. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review
Hanafi AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance
Ko et al. Comparison between conventional smear and liquid-based preparation in endoscopic ultrasonography-fine needle aspiration cytology of pancreatic lesions