Applications of Deep Learning to Neuro-Imaging Techniques

Guangming Zhu, Bin Jiang, Liz Tong, Yuan Xie, Greg Zaharchuk, Max Wintermark
<span title="2019-08-14">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4kchoxo3jrfubkck3y7nfncina" style="color: black;">Frontiers in Neurology</a> </i> &nbsp;
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering
more &raquo; ... adiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fneur.2019.00869">doi:10.3389/fneur.2019.00869</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31474928">pmid:31474928</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6702308/">pmcid:PMC6702308</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yki64mb57jhafduasd3hohfkgi">fatcat:yki64mb57jhafduasd3hohfkgi</a> </span>
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