Deep Learning: A Primer for Radiologists.

By Gabriel Chartrand, P. Cheng, Eugene Vorontsov, Michal Drozdzal, S. Turcotte, C. Pal, Samuel Kadoury, A. Tang. 2017

1,015 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [102]27, 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 [103, 104, 105, 106, 107, 108, 109, 110, 111, 1”
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation (2019, background): “semantic and instance segmentation models [1], [2], [3], [4], [5], [6].”
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, background): “The advances in the field have opened a very exciting time for collaboration of machine learning scientists and radiologists [22].”
Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, background): “Notably, there is a remarkable interest in using AI for diagnostic analysis of various types of medical images, primarily through convolutional neural networks, a type of deep learning technology referred to as “computer vision” (2-4).”
A new era of image reconstruction: TrueFidelityTM Technical white paper on deep learning image reconstruction (2019, background): “The power of DL lies in its ability to handle complex models and a vast number of parameters far beyond the abilities of human engineers and scientists.(6,7) Traditional algorithms rely on humans to manage parameters so that an optimal solution is tractable.”
Predicting cancer outcomes with radiomics and artificial intelligence in radiology (2021, background): “All these aspects have been reviewed elsewhere 81,82 .”
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, background): “AI, and more specifically machine learning, is a group of methods that is able to produce a mapping from raw inputs, such as intensities of individual pixels, to specific outputs, such as classification of a disease [113].”
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine (2018, background): “CNNs are biologically inspired networks mimicking the behaviour of the brain cortex, which contains a complex structure of cells sensitive to small regions of the visual field [3].”