AIMC Topic:
Image Interpretation, Computer-Assisted
Clear Filters Showing 1791 to 1800 of 2747 articles
Is Image Interpretation a Sustainable Form of Advanced Practice in Medical Imaging?
Journal of medical imaging and radiation sciences
Feb 1, 2019
Artificial intelligence: a challenge for third millennium radiologist.
La Radiologia medica
Feb 1, 2019
Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.
JAMA network open
Feb 1, 2019
IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.
Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network.
Journal of magnetic resonance imaging : JMRI
Jan 30, 2019
BACKGROUND: Water-fat separation is a postprocessing technique most commonly applied to multiple-gradient-echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Num...
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
PLoS medicine
Jan 24, 2019
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers....
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.
IEEE transactions on medical imaging
Jan 23, 2019
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and fo...
Deep Learning: Current and Emerging Applications in Medicine and Technology.
IEEE journal of biomedical and health informatics
Jan 23, 2019
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical ...
Attention Residual Learning for Skin Lesion Classification.
IEEE transactions on medical imaging
Jan 21, 2019
Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classif...
Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.
Journal of magnetic resonance imaging : JMRI
Jan 16, 2019
BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a m...
Automated analysis of cardiovascular magnetic resonance myocardial native T mapping images using fully convolutional neural networks.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Jan 14, 2019
BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial native T mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T are measured manually by drawing region of interest in motion-corrected T...
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