Deep Learning in Neuroradiology
By Greg Zaharchuk, Enhao Gong, M. Wintermark, Daniel L. Rubin, Curtis P. Langlotz. 2018
234 citations recorded by the source corpus.
SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.
Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Radiology, Nuclear Medicine and Imaging
- Arvid Lundervold
- Junaid Qadir
- Adeel Razi
- Adnan Qayyum
- Florian Tramèr
- Shakked Noy
- Whitney Zhang
- Dan Boneh
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
- Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “…state-of-the-art method for many tasks including classification, localisation and segmentation (Bai et al., 2017; Kamnitsas et al., 2017, 2018; Lee et al., 2015; Litjens et al., 2017; Long et al., 2015; Ronneberger et al., 2015; Roth et al., 2017, 2018; Xie and Tu, 2015; Zaharchuk et al., 2018).”
- Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, background): “Biomedical fields with large image datasets—such as radiology [47], cardiology [48], pathology [49], and genomics [50]—are using various ML”