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 |
|---|
| 2003 | 0 | 1 | 1 |
| 2004 | 2 | 1 | 3 |
| 2005 | 2 | 1 | 3 |
| 2006 | 2 | 1 | 3 |
| 2007 | 3 | 0 | 3 |
| 2008 | 2 | 1 | 3 |
| 2009 | 6 | 2 | 8 |
| 2010 | 3 | 0 | 3 |
| 2011 | 0 | 1 | 1 |
| 2012 | 2 | 2 | 4 |
| 2013 | 3 | 4 | 7 |
| 2014 | 3 | 3 | 6 |
| 2015 | 5 | 4 | 9 |
| 2016 | 3 | 1 | 4 |
| 2017 | 3 | 0 | 3 |
| 2018 | 2 | 2 | 4 |
| 2019 | 1 | 1 | 2 |
| 2020 | 2 | 0 | 2 |
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Below are the most recent publications written about "Image Interpretation, Computer-Assisted" by people in Profiles.
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Distinguishing Extravascular from Intravascular Ferumoxytol Pools within the Brain: Proof of Concept in Patients with Treated Glioblastoma. AJNR Am J Neuroradiol. 2020 07; 41(7):1193-1200.
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Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention. Neuroimage Clin. 2020; 26:102244.
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Intravascular optical coherence tomography method for automated detection of macrophage infiltration within atherosclerotic coronary plaques. Atherosclerosis. 2019 11; 290:94-102.
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Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model. Sci Rep. 2019 05 13; 9(1):7293.
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Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019 01 15; 185:140-153.
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Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol. 2018 08 06; 63(15):155020.
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Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep. 2018 04 03; 23(1):181-193.e7.
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The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage. 2018 06; 173:275-286.
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Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall. Am J Psychiatry. 2017 08 01; 174(8):748-755.
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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Programs Biomed. 2017 Jun; 144:97-104.