Image Interpretation, Computer-Assisted
"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.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Descriptor ID |
D007090
|
MeSH Number(s) |
E01.158.600 E01.370.350.350 L01.313.500.750.100.158.600
|
Concept/Terms |
Image Interpretation, Computer-Assisted- Image Interpretation, Computer-Assisted
- Computer-Assisted Image Interpretation
- Computer-Assisted Image Interpretations
- Image Interpretations, Computer-Assisted
- Interpretation, Computer-Assisted Image
- Interpretations, Computer-Assisted Image
- Image Interpretation, Computer Assisted
|
Below are MeSH descriptors whose meaning is more general than "Image Interpretation, Computer-Assisted".
Below are MeSH descriptors whose meaning is more specific than "Image Interpretation, Computer-Assisted".
This graph shows the total number of publications written about "Image Interpretation, Computer-Assisted" by people in this website by year, and whether "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 |
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1995 | 1 | 0 | 1 |
1999 | 0 | 1 | 1 |
2003 | 6 | 3 | 9 |
2004 | 5 | 2 | 7 |
2005 | 27 | 8 | 35 |
2006 | 9 | 3 | 12 |
2007 | 19 | 8 | 27 |
2008 | 12 | 12 | 24 |
2009 | 13 | 8 | 21 |
2010 | 17 | 7 | 24 |
2011 | 4 | 7 | 11 |
2012 | 15 | 12 | 27 |
2013 | 12 | 11 | 23 |
2014 | 14 | 8 | 22 |
2015 | 13 | 14 | 27 |
2016 | 13 | 8 | 21 |
2017 | 1 | 7 | 8 |
2018 | 7 | 12 | 19 |
2019 | 3 | 8 | 11 |
2020 | 3 | 5 | 8 |
2021 | 4 | 1 | 5 |
2022 | 1 | 0 | 1 |
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Below are the most recent publications written about "Image Interpretation, Computer-Assisted" by people in Profiles.
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Validating Whole Slide Imaging Systems for Diagnostic Purposes in Pathology. Arch Pathol Lab Med. 2022 04 01; 146(4):440-450.
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Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective. J Comput Assist Tomogr. 2021 Nov-Dec 01; 45(6):805-811.
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The utility of digital pathology in improving the diagnostic skills of pathology trainees in commonly encountered pigmented cutaneous lesions during the COVID-19 pandemic: A single academic institution experience. Ann Diagn Pathol. 2021 Oct; 54:151807.
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Ensuring Adequate Development and Appropriate Use of Artificial Intelligence in Pediatric Medical Imaging. AJR Am J Roentgenol. 2022 01; 218(1):182-183.
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Artificial intelligence in oncology: Path to implementation. Cancer Med. 2021 06; 10(12):4138-4149.
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Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma. Clin Radiol. 2021 Sep; 76(9):711.e1-711.e7.
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Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach. Technol Cancer Res Treat. 2021 Jan-Dec; 20:15330338211004919.
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Estimating Local Cellular Density in Glioma Using MR Imaging Data. AJNR Am J Neuroradiol. 2021 01; 42(1):102-108.
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Magnetic Resonance of Rectal Cancer Response to Therapy: An Image Quality Comparison between 3.0 and 1.5 Tesla. Biomed Res Int. 2020; 2020:9842732.
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Automated vessel density detection in fluorescein angiography images correlates with vision in proliferative diabetic retinopathy. PLoS One. 2020; 15(9):e0238958.