  The long-tailed distribution problem in medical image analysis reflects a
high prevalence of common conditions and a low prevalence of rare ones, which
poses a significant challenge in developing a unified model capable of
identifying rare or novel tumor categories not encountered during training. In
this paper, we propose a new zero-shot pan-tumor segmentation framework (ZePT)
based on query-disentangling and self-prompting to segment unseen tumor
categories beyond the training set. ZePT disentangles the object queries into
two subsets and trains them in two stages. Initially, it learns a set of
fundamental queries for organ segmentation through an object-aware feature
grouping strategy, which gathers organ-level visual features. Subsequently, it
refines the other set of advanced queries that focus on the auto-generated
visual prompts for unseen tumor segmentation. Moreover, we introduce
query-knowledge alignment at the feature level to enhance each query's
discriminative representation and generalizability. Extensive experiments on
various tumor segmentation tasks demonstrate the performance superiority of
ZePT, which surpasses the previous counterparts and evidence the promising
ability for zero-shot tumor segmentation in real-world settings.
