e-learning
resources
ERJ
Login
Search all ERS
e-learning
resources
Disease Areas
Airways Diseases
Interstitial Lung Diseases
Respiratory Critical Care
Respiratory Infections
Paediatric Respiratory Diseases
Pulmonary Vascular Diseases
Sleep and Breathing Disorders
Thoracic Oncology
Events
International Congress
Courses
Webinars
Conferences
Research Seminars
Journal Clubs
Publications
Breathe
Monograph
ERJ
ERJ Open Research
ERR
European Lung White Book
Handbook Series
Guidelines
All ERS guidelines
e-learning
CME Online
Case reports
Short Videos
SpirXpert
Procedure Videos
CME tests
Reference Database of Respiratory Sounds
Radiology Image Challenge
Brief tobacco interventions
EU Projects
VALUE-Dx
ERN-LUNG
ECRAID
UNITE4TB
Disease Areas
Events
Publications
Guidelines
e-learning
EU Projects
Login
Search
Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
Ju Gang Nam, Minchul Kim, Jongchan Park, Eui Jin Hwang, Jong Hyuk Lee, Jung Hee Hong, Jin Mo Goo, Chang Min Park
Source:
Eur Respir J, 57 (5) 2003061; 10.1183/13993003.03061-2020
Journal Issue:
May
Rating:
You must
login
to grade this presentation.
Share or cite this content
Citations should be made in the following way:
Ju Gang Nam, Minchul Kim, Jongchan Park, Eui Jin Hwang, Jong Hyuk Lee, Jung Hee Hong, Jin Mo Goo, Chang Min Park. Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs. Eur Respir J, 57 (5) 2003061; 10.1183/13993003.03061-2020
You must
login
to share this Presentation/Article on Twitter, Facebook, LinkedIn or by email.
Member's Comments
No comment yet.
You must
Login
to comment this presentation.
Related content which might interest you:
Imaging of complicated pneumonia: what is new?
Imaging of eosinophilic lung disease
N3 hilar sampling decision in the staging of mediastinal lung cancer
Related content which might interest you:
The frequency of the errors in the interpretation of digital radiographs of the chest by radiologists when detecting nodule and mass in the lungs.
Source: Virtual Congress 2020 – New imaging techniques applied to old problems
Year: 2020
A new method for evaluation of severity in COPD using dynamic chest x-ray examination
Source: Annual Congress 2012 - Functional imaging in pulmonary oncology and COPD. Radiation dose in chest CT: survey and real life
Year: 2012
Clinical utility of CT in children with persistent focal chest abnormality
Source: Eur Respir J 2005; 26: 751
Year: 2005
Electromagnetic navigation – a new approach in diagnosing peripheral lung lesions: evaluation of the learning curve
Source: Eur Respir J 2006; 28: Suppl. 50, 595s
Year: 2006
Fully automatic detection and quantification of emphysema on computed tomography of the chest by a new software – comparison with lung functional criteria
Source: Annual Congress 2008 - Imaging and measurement techniques in the evaluation of pulmonary embolism, COPD and pleural diseases
Year: 2008
Development and validation of a low-cost chest wall motion assessment system
Source: International Congress 2015 – Lung function: exploring the boundaries of the respiratory system
Year: 2015
A robust lung segmentation algorithm using fuzzy C-means method from HRCT scans
Source: International Congress 2016 – Imaging of COPD and airways: structural and functional assessments
Year: 2016
The use of a simplified chest X-ray evaluation score for the assessment of tuberculosis
Source: International Congress 2019 – Tuberculosis: from diagnosis to complications
Year: 2019
The value of chest CT as a COVID-19 screening tool in children
Source: Eur Respir J, 55 (6) 2001241; 10.1183/13993003.01241-2020
Year: 2020
Novel quantitative bronchiectasis scoring technique for chest computed tomography: BEST-CT. A study within the iABC project
Source: International Congress 2019 – Imaging for prognostication and disease characterisation: 2019 update
Year: 2019
Chest radiography: performance, indications and interpretation
Source: Eur Respir Monogr 2015; 70: 34-46
Year: 2015
Computer-assisted detection of pulmonary embolism: performance evaluation in consensus with experienced and inexperienced chest radiologists
Source: Eur Respir J 2007; 30: Suppl. 51, 253s
Year: 2007
The validity of classic symptoms, medical history, and chest radiographs in predicting pulmonary tuberculosis (the TB scoring system)
Source: Eur Respir J 2004; 24: Suppl. 48, 654s
Year: 2004
A new method for detection of flow limitation in COPD using dynamic chest X-ray examination
Source: Annual Congress 2011 - Morphological and functional imaging in obstructive airway disease
Year: 2011
Deep learning architecture for the classification of COVID-19 and others pneumonias sources on lung CT imaging
Source: Virtual Congress 2021 – Imaging
Year: 2021
Late Breaking Abstract - Identifying and phenotyping COVID-19 patients using machine learning on chest x-rays
Source: Virtual Congress 2020 – Covering COVID - the best abstracts
Year: 2020
Diagnostic accuracy of COPD severity grading using machine learning features and lung sounds.
Source: International Congress 2019 – Innovations in primary care assessment and management
Year: 2019
Interobserver variability in visual evaluation of thoracic CT scans and comparison with automatic computer measurements of CT lung density
Source: Annual Congress 2012 - Functional imaging in pulmonary oncology and COPD. Radiation dose in chest CT: survey and real life
Year: 2012
Feasibility of low dose chest CT for virtual bronchoscopy navigation in a porcine model
Source: International Congress 2019 – Interventional pulmonology techniques: targeting selective lung tissue
Year: 2019
Diagnostic accuracy of asthma severity grading using machine learning features and lung sounds
Source: International Congress 2018 – Innovations in equipment and their application
Year: 2018
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking "Accept", you consent to the use of the cookies.
Accept