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Self-supervised learning framework application for medical image analysis: a review and summary.

Xiangrui Zeng1, Nibras Abdullah2, Putra Sumari3

  • 1School of Computer Sciences, Universiti Sains Malaysia, USM, 11800, Pulau Pinang, Malaysia. xavierzeng@student.usm.my.

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Summary
This summary is machine-generated.

Self-supervised learning reduces manual annotation for medical AI by using unlabeled data. This review examines its methods across CT, MRI, and X-ray imaging, guiding future research.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning

Background:

  • Manual annotation of medical images is time-consuming, costly, and introduces bias.
  • The rapid growth of medical data hinders supervised learning progress.
  • Self-supervised learning (SSL) offers a solution by leveraging unlabeled data.

Purpose of the Study:

  • To provide a comprehensive review of SSL methodologies in medical imaging.
  • To systematically examine SSL applications from 2018 to September 2024.
  • To guide medical professionals in integrating SSL into their research.

Main Methods:

  • Systematic literature review of SSL in medical imaging.
  • Analysis of studies across various modalities (CT, MRI, X-ray, Histology, Ultrasound).
  • Categorization of applications including classification, segmentation, and performance enhancement.

Main Results:

  • CT and MRI dominate SSL research, followed by X-ray, Histology, and Ultrasound.
  • Contrastive learning is more prevalent than generative learning, except for CT and MRI.
  • Segmentation tasks and MRI/Ultrasound classification show potential for further development.

Conclusions:

  • SSL effectively addresses the limitations of manual annotation in medical imaging.
  • The review highlights research trends and identifies areas for future SSL exploration.
  • SSL holds significant promise for advancing AI in healthcare.