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A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical

Wei Chen1, Chen Li1, Dan Chen2

  • 1School of Computer, National University of Defense Technology, Changsha 410073, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 22, 2023
PubMed
Summary

TOWER enhances self-supervised learning for biomedical microscopy images by diversifying sample spaces and improving representation learning. This knowledge-based framework achieves superior performance in image recognition and segmentation tasks.

Keywords:
Biomedical microscopy imagesClassificationContrastive learningGenerative learningSegmentationSelf-supervised neural network pre-training

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Area of Science:

  • Biomedical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Self-supervised pre-training is crucial for automated recognition of biomedical microscopy images.
  • Current methods face challenges in learning robust representations from low-diversity, unlabeled data and achieving high-quality segmentation.

Purpose of the Study:

  • To propose a knowledge-based learning framework (TOWER) for enhanced recognition of biomedical microscopy images.
  • To address limitations in unsupervised representation learning and semantic segmentation for annotation-free images.

Main Methods:

  • TOWER utilizes a three-phase approach synergizing contrastive and generative learning.
  • Phase 1: Sample Space Diversification using reconstructive proxy tasks to embed prior knowledge.
  • Phase 2: Enhanced Representation Learning with informative noise-contrastive estimation loss.
  • Phase 3: Correlated Optimization of encoder and decoder via image restoration for semantic segmentation.

Main Results:

  • TOWER statistically outperforms state-of-the-art self-supervised methods, including SimCLR and BYOL.
  • Achieved a 1.38 percentage point Dice improvement over SimCLR.
  • Demonstrated potential in multi-modality medical image analysis and label-efficient semi-supervised learning.

Conclusions:

  • TOWER offers a robust framework for self-supervised learning in biomedical image analysis.
  • The method significantly improves image recognition and segmentation accuracy.
  • TOWER reduces annotation costs by up to 99% in pathological classification, enabling efficient semi-supervised learning.