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STAMP: A Self-training Student-Teacher Augmentation-Driven Meta Pseudo-Labeling Framework for 3D Cardiac MRI Image

S M Kamrul Hasan1, Cristian Linte2

  • 1Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)
|May 1, 2023
PubMed
Summary

This study introduces STAMP, a semi-supervised learning framework that reduces pseudo-labeling bias in medical image segmentation. STAMP improves segmentation accuracy by adapting a teacher network and using data augmentation, outperforming existing methods.

Keywords:
Cardiac MRI segmentationConfidence thresholdMeta pseudo-labelStudent-teacher modelWeak and strong augment

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning enhances medical image segmentation.
  • Semi-supervised learning (SSL) leverages unlabeled data for improved performance.
  • Pseudo-labeling bias is a key challenge in SSL for segmentation.

Purpose of the Study:

  • To propose STAMP, a novel SSL framework to mitigate pseudo-labeling bias in medical image segmentation.
  • To enhance model generalization and reduce error rates through adaptive pseudo-labeling and data augmentation.

Main Methods:

  • Developed STAMP (Student-Teacher Augmentation-driven consistency regularization via Meta Pseudo-Labeling) framework.
  • Employed a meta pseudo-labeling strategy where the Teacher network adapts based on Student network performance.
  • Utilized strong and weak data augmentation policies for consistent probability distribution output.

Main Results:

  • STAMP demonstrated effectiveness in segmenting left atrial cavities from GE-MR images.
  • Achieved a 2.6% improvement in Dice and 4.4% improvement in Jaccard over state-of-the-art SSL methods.
  • Outperformance was observed using only 10% labeled data, highlighting efficient unlabeled data exploitation.

Conclusions:

  • STAMP effectively addresses pseudo-labeling bias in semi-supervised medical image segmentation.
  • The framework shows significant potential for improving segmentation accuracy with limited labeled data.
  • STAMP offers a robust solution for leveraging unlabeled data in medical imaging tasks.