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How Well Do Self-Supervised Models Transfer to Medical Imaging?

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  • 1Department of Computing, Imperial College London, London SW7 2AZ, UK.

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Self-supervised learning models pretrained on ImageNet show better generalization on medical images than supervised models. However, models trained in-domain excel on specific tasks but lack broader applicability.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Self-supervised learning (SSL) has shown promise in transferring knowledge between similar medical imaging datasets.
  • A large-scale comparison of different SSL models' transferability across diverse medical imaging datasets is lacking.
  • Understanding model generalizability is crucial for effective deployment in clinical settings.

Purpose of the Study:

  • To compare the generalizability of seven self-supervised models against supervised baselines across eight medical datasets.
  • To evaluate the performance of in-domain versus general-domain pre-trained self-supervised models on medical classification tasks.
  • To investigate the impact of pre-training strategies on feature representation and model transferability in medical imaging.

Main Methods:

  • Seven self-supervised models were evaluated, including two trained on in-domain medical data.
  • Performance was compared against supervised learning baselines.
  • Models were tested across eight diverse medical imaging datasets for classification tasks.

Main Results:

  • ImageNet-pretrained self-supervised models demonstrated superior generalizability, achieving up to 10% higher scores on medical classification tasks compared to supervised models.
  • In-domain pre-trained models significantly outperformed others (over 20%) on their specific training tasks but showed reduced accuracy on other datasets.
  • Analysis of feature representations suggested that in-domain models may overfit to specific image regions, limiting broader transferability.

Conclusions:

  • Self-supervised learning models, particularly those pre-trained on large general datasets like ImageNet, offer enhanced generalizability for medical imaging tasks.
  • While in-domain pre-training boosts performance on specific medical tasks, it compromises broad applicability, highlighting a trade-off in model specialization.
  • Future research should focus on developing SSL strategies that balance domain-specific feature learning with robust generalizability across diverse medical imaging applications.