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Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

IEEE Transactions on Medical Imaging · 2016 · Vol. 35(5) · pp. 1153–1159
Hayit GreenspanBram van GinnekenRonald M. Summers

Abstract

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.

AI in cancer detectionCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingDeep learningArtificial intelligenceComputer scienceConvolutional neural networkAbstractionMachine learningMedical imagingField (mathematics)Deep neural networksRaw data

Funding

  • National Institutes of Health
Citations
1,688
FWCI
295.94
field-weighted impact
References
38
Percentile
100%
vs. same field & year
Citations per year
References
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Neural Networks · 2014 · 17,774 citations
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Neural Computation · 2006 · 16,253 citations
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
IEEE Transactions on Medical Imaging · 2016 · 2,615 citations
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