用双教师+双复制粘贴提升3D医学图像分割的半监督效果
Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentation
- 双教师模型结合双复制粘贴技术，增强教师间多样性
- 在多个3D医学数据集上达到领先性能，边界分割精度显著提升
- 适合需要高精度分割的医疗影像研究者使用
半监督学习（SSL）缓解3D医学图像分割中的标注成本问题，常用方法为教师-学生框架。然而，单教师模型中采用指数移动平均（EMA）可能导致师生权重耦合，削弱教师的知识传递能力。双教师模型虽可缓解此问题，但常忽视教师间的多样性保持，导致教师间仍存在耦合。为此，本文引入双复制粘贴（DCP）技术以增强教师多样性。同时，提出分阶段选择性集成（SSE）模块，根据样本特性动态选择集成策略，提升标签边界分割精度，从而改善伪标签质量。实验结果表明，所提方法在多个3D医学图像分割任务中表现优异。
原文摘要 · Abstract (English)
Semi-supervised learning (SSL) techniques address the high labeling costs in 3D medical image segmentation, with the teacher-student model being a common approach. However, using an exponential moving average (EMA) in single-teacher models may cause coupling issues, where the weights of the student and teacher models become similar, limiting the teacher's ability to provide additional knowledge for the student. Dual-teacher models were introduced to address this problem but often neglected the importance of maintaining teacher model diversity, leading to coupling issues among teachers. To address the coupling issue, we incorporate a double-copy-paste (DCP) technique to enhance the diversity among the teachers. Additionally, we introduce the Staged Selective Ensemble (SSE) module, which selects different ensemble methods based on the characteristics of the samples and enables more accurate segmentation of label boundaries, thereby improving the quality of pseudo-labels. Experimental results demonstrate the effectiveness of our proposed method in 3D medical image segmentation tasks. Here is the code link: https://github.com/Fazhan-cs/DCP.
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