Vol. 4 No. 1 (2024): Journal of Machine Learning for Healthcare Decision Support
Articles

Deep Learning-based Radiomics Analysis for Prognostic Prediction in Medical Imaging: Utilizes deep learning-based radiomics analysis to extract quantitative features from medical images for prognostic prediction, facilitating personalized treatment planning and patient stratification

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  • Dr. Xavier Lefebvre
Dr. Xavier Lefebvre
Associate Professor of Data Science, École Polytechnique Fédérale de Lausanne, Switzerland
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Published 07-06-2024

Keywords

  • Deep learning,
  • radiomics analysis,
  • prognostic prediction,
  • medical imaging,
  • personalized medicine,
  • treatment planning,
  • patient stratification,
  • quantitative features,
  • machine learning
    • ...More
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Xavier Lefebvre, “Deep Learning-based Radiomics Analysis for Prognostic Prediction in Medical Imaging: Utilizes deep learning-based radiomics analysis to extract quantitative features from medical images for prognostic prediction, facilitating personalized treatment planning and patient stratification”, Journal of Machine Learning for Healthcare Decision Support, vol. 4, no. 1, pp. 83–92, Jun. 2024, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/18

Abstract

Medical imaging plays a crucial role in the diagnosis, treatment planning, and monitoring of various diseases. Traditional image analysis methods often rely on qualitative assessments by radiologists, which can be subjective and may not fully utilize the wealth of information present in medical images. Radiomics, a rapidly evolving field, aims to extract and analyze quantitative features from medical images to provide valuable insights into the underlying biology of diseases. Deep learning, a subset of machine learning, has shown remarkable success in various image analysis tasks, including medical image analysis.

This research paper focuses on the application of deep learning-based radiomics analysis for prognostic prediction in medical imaging. We explore how deep learning algorithms can be used to extract quantitative features from medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), and how these features can be used to predict patient outcomes. By leveraging the vast amounts of data present in medical images, deep learning-based radiomics analysis has the potential to revolutionize personalized medicine by providing clinicians with valuable information for treatment planning and patient stratification.

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