全微分双向双任务协同学习用于半监督3D医学图像分割
Fully Differentiable Bidirectional Dual-Task Synergistic Learning for Semi-Supervised 3D Medical Image Segmentation
AI总结：
本文提出全微分双向双任务协同学习框架，通过整合四个关键SSL组件提升3D医学图像分割性能，实现双向任务协同学习，推动半监督学习框架发展。
摘要：
半监督学习通过利用未标记数据来减轻图像分割对大规模像素级标注数据的依赖。高质量标注数据的匮乏仍然是医学图像分析中的主要挑战，这归因于高标注成本和需要专门的临床专业知识。半监督学习在解决这一瓶颈方面显示出显著潜力，伪标注和一致性正则化已成为两种主要范式。双任务协同学习，一种新兴的一致性感知范式，旨在通过建立相关任务之间的预测一致性来获得补充监督。然而，当前的方法局限于单向交互机制（通常为回归到分割），因为分割结果只能通过离线方式转换为回归输出，从而无法充分利用在线双向跨任务协作的潜在好处。因此，我们提出了一个全微分双向协同学习（DBiSL）框架，该框架无缝整合并增强了四个关键的SSL组件：监督学习、一致性正则化、伪监督学习和不确定性估计。在两个基准数据集上的实验表明，我们的方法在性能上达到了最先进的水平。除了技术贡献外，这项工作还为统一的SSL框架设计提供了新的见解，并建立了新的双任务驱动SSL架构基础，同时提供了一个通用的多任务学习框架，适用于更广泛的计算机视觉应用。代码将在接受后发布在github上。
英文摘要：
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. The scarcity of high-quality labeled data remains a major challenge in medical image analysis due to the high annotation costs and the need for specialized clinical expertise. Semi-supervised learning has demonstrated significant potential in addressing this bottleneck, with pseudo-labeling and consistency regularization emerging as two predominant paradigms. Dual-task collaborative learning, an emerging consistency-aware paradigm, seeks to derive supplementary supervision by establishing prediction consistency between related tasks. However, current methodologies are limited to unidirectional interaction mechanisms (typically regression-to-segmentation), as segmentation results can only be transformed into regression outputs in an offline manner, thereby failing to fully exploit the potential benefits of online bidirectional cross-task collaboration. Thus, we propose a fully Differentiable Bidirectional Synergistic Learning (DBiSL) framework, which seamlessly integrates and enhances four critical SSL components: supervised learning, consistency regularization, pseudo-supervised learning, and uncertainty estimation. Experiments on two benchmark datasets demonstrate our method's state-of-the-art performance. Beyond technical contributions, this work provides new insights into unified SSL framework design and establishes a new architectural foundation for dual-task-driven SSL, while offering a generic multitask learning framework applicable to broader computer vision applications. The code will be released on github upon acceptance.
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。
