Radiographic Image Interpretation, Computer-Assisted
"Radiographic Image Interpretation, Computer-Assisted" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Computer systems or networks designed to provide radiographic interpretive information.
Descriptor ID |
D011857
|
MeSH Number(s) |
E01.158.600.680 E01.370.350.350.700 E01.370.350.700.705 L01.313.500.750.100.158.600.680
|
Concept/Terms |
|
Below are MeSH descriptors whose meaning is more general than "Radiographic Image Interpretation, Computer-Assisted".
- Analytical, Diagnostic and Therapeutic Techniques and Equipment [E]
- Diagnosis [E01]
- Diagnosis, Computer-Assisted [E01.158]
- Image Interpretation, Computer-Assisted [E01.158.600]
- Radiographic Image Interpretation, Computer-Assisted [E01.158.600.680]
- Diagnostic Techniques and Procedures [E01.370]
- Diagnostic Imaging [E01.370.350]
- Image Interpretation, Computer-Assisted [E01.370.350.350]
- Radiographic Image Interpretation, Computer-Assisted [E01.370.350.350.700]
- Radiography [E01.370.350.700]
- Radiographic Image Interpretation, Computer-Assisted [E01.370.350.700.705]
- Information Science [L]
- Information Science [L01]
- Informatics [L01.313]
- Medical Informatics [L01.313.500]
- Medical Informatics Applications [L01.313.500.750]
- Decision Making, Computer-Assisted [L01.313.500.750.100]
- Diagnosis, Computer-Assisted [L01.313.500.750.100.158]
- Image Interpretation, Computer-Assisted [L01.313.500.750.100.158.600]
- Radiographic Image Interpretation, Computer-Assisted [L01.313.500.750.100.158.600.680]
Below are MeSH descriptors whose meaning is more specific than "Radiographic Image Interpretation, Computer-Assisted".
This graph shows the total number of publications written about "Radiographic Image Interpretation, Computer-Assisted" by people in this website by year, and whether "Radiographic Image Interpretation, Computer-Assisted" was a major or minor topic of these publications.
To see the data from this visualization as text,
click here.
Year | Major Topic | Minor Topic | Total |
---|
2005 | 0 | 1 | 1 |
2007 | 1 | 1 | 2 |
2008 | 0 | 1 | 1 |
2009 | 0 | 1 | 1 |
2014 | 1 | 0 | 1 |
2015 | 2 | 2 | 4 |
2016 | 1 | 2 | 3 |
2017 | 20 | 18 | 38 |
2018 | 21 | 20 | 41 |
2019 | 7 | 7 | 14 |
2020 | 6 | 14 | 20 |
2021 | 5 | 0 | 5 |
To return to the timeline, click here.
Below are the most recent publications written about "Radiographic Image Interpretation, Computer-Assisted" by people in Profiles.
-
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19. Sci Rep. 2021 09 01; 11(1):15523.
-
ANFIS-Net for automatic detection of COVID-19. Sci Rep. 2021 08 27; 11(1):17318.
-
A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study. IEEE J Biomed Health Inform. 2021 07; 25(7):2353-2362.
-
Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19. IEEE J Biomed Health Inform. 2021 07; 25(7):2363-2373.
-
COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks. IEEE J Biomed Health Inform. 2021 07; 25(7):2376-2387.
-
MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT. Comput Med Imaging Graph. 2021 09; 92:101957.
-
COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system. PLoS One. 2021; 16(6):e0252440.
-
Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images. J Healthc Eng. 2021; 2021:5513679.
-
COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images. J Healthc Eng. 2021; 2021:6658058.
-
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images. IEEE J Biomed Health Inform. 2021 05; 25(5):1336-1346.