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The success of automated medical image analysis depends on large-scale andexpert-annotated training sets. Unsupervised domain adaptation (UDA) has beenraised as a promising approach to alleviate the burden of labeled datacollection. However, they generally operate under the closed-set adaptationsetting assuming an identical label set between the source and target domains,which is over-restrictive in clinical practice where new classes commonly existacross datasets due to taxonomic inconsistency. While several methods have beenpresented to tackle both domain shifts and incoherent label sets, none of themtake into account the common characteristics of the two issues and consider thelearning dynamics along network training. In this work, we propose optimizationtrajectory distillation, a unified approach to address the two technicalchallenges from a new perspective. It exploits the low-rank nature of gradientspace and devises a dual-stream distillation algorithm to regularize thelearning dynamics of insufficiently annotated domain and classes with theexternal guidance obtained from reliable sources. Our approach resolves theissue of inadequate navigation along network optimization, which is the majorobstacle in the taxonomy adaptive cross-domain adaptation scenario. We evaluatethe proposed method extensively on several tasks towards various endpoints withclinical and open-world significance. The results demonstrate its effectivenessand improvements over previous methods.

Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation