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Deep Learning in Medical Imaging: General Overview.

June-Goo Lee1, Sanghoon Jun2,3, Young-Won Cho2,3

  • 1Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.

Korean Journal of Radiology
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Summary
This summary is machine-generated.

Artificial neural networks (ANNs), a machine learning technique, are now viable due to big data and enhanced computing power. Their potential in medical imaging offers future healthcare applications.

Keywords:
Artificial intelligenceComputer-aidedConvolutional neural networkMachine learningPrecision medicineRadiologyRecurrent Neural Network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  • Medical Imaging

Background:

  • Artificial Neural Networks (ANNs), inspired by human neuronal systems, emerged in the 1950s but faced limitations.
  • Previous challenges included vanishing gradients, overfitting, insufficient computing power, and lack of data.
  • Renewed interest is driven by big data availability, powerful graphics processing units (GPUs), and advanced deep learning algorithms.

Purpose of the Study:

  • To review the history and development of deep learning technology.
  • To explore the current and future applications of deep learning, with a focus on medical imaging.
  • To provide perspectives on the evolution and potential of ANNs in healthcare.

Main Methods:

  • Review of historical development of artificial neural networks.
  • Analysis of factors enabling recent advancements in deep learning.
  • Exploration of current research and potential applications in medical imaging.

Main Results:

  • Deep learning models show potential to surpass human performance in visual and auditory recognition tasks.
  • Advancements in computing power and data availability have overcome previous limitations of ANNs.
  • Significant potential for AI applications in medicine and healthcare, particularly in medical imaging analysis.

Conclusions:

  • Deep learning technology has evolved significantly, overcoming earlier constraints.
  • The resurgence of ANNs is poised to revolutionize medical imaging and other healthcare sectors.
  • Future applications of deep learning in medical diagnostics and treatment are highly promising.