DuEDL：用于涂鸦标注医学图像分割的双分支证据深度学习
DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation
AI总结：
针对涂鸦标注医学图像分割中准确性与可靠性权衡问题，提出双分支证据深度学习框架DuEDL，通过双分支证据融合和联合损失训练，在不牺牲准确性的前提下提升模型可靠性与泛化能力。
摘要：
尽管基于涂鸦标注的医学图像分割近期取得了进展，但大多数模型在开放环境中的分割结果仍不够稳健和泛化。证据深度学习（EDL）最近被提出作为建模预测不确定性并提高医学图像分割可靠性的有前景的解决方案。然而，将EDL直接应用于涂鸦监督的医学图像分割面临准确性与可靠性之间的权衡。为解决这一挑战，我们提出了一种名为双分支证据深度学习（DuEDL）的新框架。首先，将分割网络的解码器改为两个不同的分支，并融合两个分支的证据以生成高质量的伪标签。然后，该框架应用部分证据损失和双分支一致性损失进行模型的联合训练，以适应涂鸦监督学习。所提方法在两个心脏数据集ACDC和MSCMRseg上进行了测试。结果表明，我们的方法在不牺牲准确性的情况下显著增强了模型的可靠性和泛化能力，优于最先进的基线方法。代码可在https://github.com/Gardnery/DuEDL获取。
英文摘要：
Despite the recent progress in medical image segmentation with scribble-based annotations, the segmentation results of most models are still not ro-bust and generalizable enough in open environments. Evidential deep learn-ing (EDL) has recently been proposed as a promising solution to model predictive uncertainty and improve the reliability of medical image segmen-tation. However directly applying EDL to scribble-supervised medical im-age segmentation faces a tradeoff between accuracy and reliability. To ad-dress the challenge, we propose a novel framework called Dual-Branch Evi-dential Deep Learning (DuEDL). Firstly, the decoder of the segmentation network is changed to two different branches, and the evidence of the two branches is fused to generate high-quality pseudo-labels. Then the frame-work applies partial evidence loss and two-branch consistent loss for joint training of the model to adapt to the scribble supervision learning. The pro-posed method was tested on two cardiac datasets: ACDC and MSCMRseg. The results show that our method significantly enhances the reliability and generalization ability of the model without sacrificing accuracy, outper-forming state-of-the-art baselines. The code is available at https://github.com/Gardnery/DuEDL.
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。
