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The papers in this special section focus on the technology and applications supported by deep learning. Deep learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications.
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Hayit Greenspan
Tel Aviv University
Bram van Ginneken
University of Copenhagen
Ronald M. Summers
National Institutes of Health Clinical Center
IEEE Transactions on Medical Imaging
Radboud University Nijmegen
Tel Aviv University
Radboud University Medical Center
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Greenspan et al. (Fri,) studied this question.
synapsesocial.com/papers/6a029040bc3ffe278e650ba5 — DOI: https://doi.org/10.1109/tmi.2016.2553401
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