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Special Edition: Deep Learning in Medical Imaging, May 2016

Special Issue Editors: H. Greenspan, B. van Ginneken, R. Summers
IEEE Trans Med Imaging, vol. 35, issue 5, May 2016

Deep learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. 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 automa tically 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 effect ive 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. Promising results are emerging.

Be sure to read the entire May issue at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=42