mlsanityData to 14 Oct 2026

WT-BCP: Wavelet Transform based Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

arXiv 2504.14445 ↗First posted 20 Apr 2025cs.CVcited by 0

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Abstract (arXiv)

Semi-supervised medical image segmentation (SSMIS) shows promise in reducing reliance on scarce labeled medical data. However, SSMIS field confronts challenges such as distribution mismatches between labeled and unlabeled data, artificial perturbations causing training biases, and inadequate use of raw image information, especially low-frequency (LF) and high-frequency (HF) components.To address these challenges, we propose a Wavelet Transform based Bidirectional Copy-Paste SSMIS framework, named WT-BCP, which improves upon the Mean Teacher approach. Our method enhances unlabeled data understanding by copying random crops between labeled and unlabeled images and employs WT to extract LF and HF details.We propose a multi-input and multi-output model named XNet-Plus, to receive the fused information after WT. Moreover, consistency training among multiple outputs helps to mitigate learning biases introduced by artificial perturbations. During consistency training, the mixed images resulting from WT are fed into both models, with the student model's output being supervised by pseudo-labels and ground-truth. Extensive experiments conducted on 2D and 3D datasets confirm the effectiveness of our model.Code: https://github.com/simzhangbest/WT-BCP.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 20 Apr 2025.

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References · 25

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  11. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  12. 2023Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentationcited by 116
13 more
  1. 2021Semi-supervised Left Atrium Segmentation with Mutual Consistency Trainingcited by 104
  2. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  3. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
  4. 2020Unpaired Multi-modal Segmentation via Knowledge Distillationcited by 81
  5. 2022Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learningcited by 78
  6. 2021Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentationcited by 61
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  8. 2022UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentationcited by 45
  9. 2018Wavelet Convolutional Neural Networkscited by 29
  10. 2021WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classificationcited by 29
  11. 20203D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Managementcited by 19
  12. 2021Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotationcited by 11
  13. 2024XNet v2: Fewer Limitations, Better Results and Greater Universalitycited by 5

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