用不确定性加权集成学习，提升心脏影像末端切片分割精度。
Uncertainty-Based Ensemble Learning in CMR Semantic Segmentation
- 基于分割结果方差计算全局不确定性，动态调整模型权重。
- 在ACDC和M&Ms数据集上，末端切片准确率显著提升，整体性能接近顶尖水平。
- 适合关注临床精准分割、尤其是末段心室分析的研究者。
现有方法从心脏电影序列中通过心室语义分割提取临床功能指标。尽管整体分割表现良好，但在末端切片上仍存在困难。为此，我们通过分割方差提取全局不确定性，并将其用于集成学习框架Streaming中，以实现分类器加权，平衡整体与末端切片的性能。我们引入末端系数（End Coefficient, EC）量化末端切片分割精度。在ACDC与M&Ms数据集上的实验表明，本框架实现了接近当前最优的骰子相似系数（DSC），并在末端切片性能上超越所有对比模型，显著提升了患者特异性分割准确性。代码已开源：https://github.com/LEw1sin/Uncertainty-Ensemble。
原文摘要 · Abstract (English)
Existing methods derive clinical functional metrics from ventricular semantic segmentation in cardiac cine sequences. While performing well on overall segmentation, they struggle with the end slices. To address this, we extract global uncertainty from segmentation variance and use it in our ensemble learning method, Streaming, for classifier weighting, balancing overall and end-slice performance. We introduce the End Coefficient (EC) to quantify end-slice accuracy. Experiments on ACDC and M\&Ms datasets show that our framework achieves near state-of-the-art Dice Similarity Coefficient (DSC) and outperforms all models on end-slice performance, improving patient-specific segmentation accuracy. We open-sourced our code on https://github.com/LEw1sin/Uncertainty-Ensemble.
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