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Semi-supervised Learning with Robust Loss in Brain Segmentation

arXiv 2212.03082 ↗First posted 3 Dec 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

In this work, we used a semi-supervised learning method to train deep learning model that can segment the brain MRI images. The semi-supervised model uses less labeled data, and the performance is competitive with the supervised model with full labeled data. This framework could reduce the cost of labeling MRI images. We also introduced robust loss to reduce the noise effects of inaccurate labels generated in semi-supervised learning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 6 references that are in the map. First posted on arXiv 3 Dec 2022.

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References · 6

  1. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  3. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  4. 2018Exploring the Limits of Weakly Supervised Pretrainingcited by 604
  5. 2022Semi-supervised Learning using Robust Losscited by 4
  6. 2022Learning from imperfect training data using a robust loss function: application to brain image segmentationcited by 2

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