通过集成蒸馏实现不确定性感知的视网膜血管分割
Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation
AI总结：
本文提出集成蒸馏方法，将多个集成模型知识蒸馏至单一模型，在DRIVE和FIVES数据集上以更低计算复杂度实现与深度集成相当的视网膜血管分割及不确定性估计性能。
摘要：
不确定性估计对于可靠的医学图像分割至关重要，尤其是在视网膜血管分析中，准确的预测对诊断应用必不可少。深度集成通过单独训练多个网络，被广泛用于提升医学图像分割性能。然而，训练和测试成本会随集成数量增加而上升。在本研究中，我们提出集成蒸馏作为常用不确定性估计技术的稳健替代方案，将多个集成模型的知识蒸馏到单一模型中。通过在 DRIVE 和 FIVES 数据集上进行的大量实验，我们证明集成蒸馏在校准和分割指标上取得了相当的性能，同时显著降低了计算复杂度。这些发现表明，集成蒸馏为视网膜血管分割中的不确定性估计提供了高效可靠的方法，使其成为医学成像应用的有前景工具。
英文摘要：
Uncertainty estimation is critical for reliable medical image segmentation, particularly in retinal vessel analysis, where accurate predictions are essential for diagnostic applications. Deep Ensembles, where multiple networks are trained individually, are widely used to improve medical image segmentation performance. However, training and testing costs increase with the number of ensembles. In this work, we propose Ensemble Distillation as a robust alternative to commonly used uncertainty estimation techniques by distilling the knowledge of multiple ensemble models into a single model. Through extensive experiments on the DRIVE and FIVES datasets, we demonstrate that Ensemble Distillation achieves comparable performance via calibration and segmentation metrics, while significantly reducing computational complexity. These findings suggest that Ensemble distillation provides an efficient and reliable approach for uncertainty estimation in the segmentation of the retinal vessels, making it a promising tool for medical imaging applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
