The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. However, they generally operate under the closed-set adaptation setting assuming an identical label set between the source and target domains, which is over-restrictive in clinical practice where new classes commonly exist across datasets due to taxonomic inconsistency. While several methods have been presented to tackle both domain shifts and incoherent label sets, none of them take into account the common characteristics of the two issues and consider the learning dynamics along network training. In this work, we propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective. It exploits the low-rank nature of gradient space and devises a dual-stream distillation algorithm to regularize the learning dynamics of insufficiently annotated domain and classes with the external guidance obtained from reliable sources. Our approach resolves the issue of inadequate navigation along network optimization, which is the major obstacle in the taxonomy adaptive cross-domain adaptation scenario. We evaluate the proposed method extensively on several tasks towards various endpoints with clinical and open-world significance. The results demonstrate its effectiveness and improvements over previous methods.

翻译：自动化医学影像分析的成功依赖于大规模且经专家标注的训练集。无监督域适应（UDA）被视为减轻标注数据收集负担的有效方法。然而，该类方法通常在闭合集适应设定下工作，假设源域与目标域具有相同的标签集，这在临床实践中过于严苛——由于分类学不一致，不同数据集之间常存在新类别。尽管已有多种方法同时处理域偏移和标签集不一致问题，但尚未有研究考虑这两个问题的共性及其在网络训练过程中的动力学特性。本文提出优化轨迹蒸馏，一种从新视角统一解决这两项技术挑战的方法。该方法利用梯度空间的低秩特性，设计双流蒸馏算法，通过从可靠来源获取的外部引导来规范标注不足的域和类别的学习动态。我们的方法解决了网络优化过程中导航不足的问题——这正是分类自适应跨域迁移场景的主要障碍。我们在多个面向临床和开放世界意义的任务上对该方法进行了广泛评估。结果表明，该方法相较于既有方法具有显著有效性和改进。