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Updated: Oct 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

Explainable Deep Learning Models in Medical Image Analysis.

Amitojdeep Singh1,2, Sourya Sengupta1,2, Vasudevan Lakshminarayanan1,2

  • 1Theoretical and Experimental Epistemology Laboratory, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

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Explainable deep learning (XDL) enhances medical AI by revealing decision factors, overcoming the black-box problem for clinical use. This review covers XDL applications, challenges, and future research in medical imaging.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Explainable Deep Learning

Background:

  • Deep learning (DL) excels in medical diagnostics, sometimes surpassing human experts.
  • The "black-box" nature of DL hinders clinical adoption.
  • Explainability studies aim to identify key features influencing DL model decisions.

Purpose of the Study:

  • To review current applications of explainable deep learning in medical imaging.
  • To discuss practical challenges for clinical deployment of explainable AI.
  • To identify areas for future research in explainable deep learning for clinical end-users.

Main Methods:

  • Literature review of explainable deep learning in medical imaging.
  • Analysis of current approaches and their clinical applications.
Keywords:
XAIdeep learningdiagnosisexplainabilityexplainable AImedical imaging

Related Experiment Videos

Last Updated: Oct 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K
  • Discussion of practical challenges and future research directions.

    • Main Results:

    • Explainable deep learning is increasingly applied across various medical imaging tasks.
    • Key challenges include interpretability, validation, and integration into clinical workflows.
    • Further research is needed to bridge the gap between DL development and clinical end-user needs.

    Conclusions:

    • Explainable deep learning offers promising solutions to enhance trust and adoption of AI in clinical settings.
    • Addressing practical challenges is crucial for successful clinical deployment.
    • Continued research focusing on user-centric design and validation is essential.