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[Progress in biomedical data analysis based on deep learning].

Suyi Li1, Shijie Tang1, Feng Li1

  • 1College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|April 25, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning offers new solutions for analyzing big biomedical data, improving medical diagnosis. This review covers methods, applications, and future directions for deep learning in biomedical analysis.

Keywords:
biomedical sciencedata analysisdeep learning

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Area of Science:

  • Biomedical data analysis
  • Artificial Intelligence
  • Machine Learning

Background:

  • Big data presents challenges for traditional biomedical analysis.
  • Deep learning (DL) offers significant opportunities for advancing biomedical data analysis.

Purpose of the Study:

  • To review recent advancements in deep learning for biomedical data analysis.
  • To highlight DL applications in medical assistant diagnosis.
  • To explore future research directions.

Main Methods:

  • Introduction to deep learning methodologies and frameworks.
  • Chronological summary of DL applications in biomedical data analysis over the past five years.
  • Focus on problem definition, data preprocessing, model building, and training algorithms.

Main Results:

  • Deep learning is increasingly applied to complex biomedical datasets.
  • Significant progress has been made in medical assistant diagnosis using DL.
  • Established DL frameworks and algorithms are being adapted for biomedical tasks.

Conclusions:

  • Deep learning is a transformative technology in biomedical data analysis.
  • Continued research is needed to optimize DL models for specific biomedical challenges.
  • Future developments may focus on enhanced diagnostic accuracy and personalized medicine.