A single biomedical image can be segmented in multiple valid ways, depending on the application. For instance, a brain MRI may be divided according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology. Existing automatic segmentation models typically either (1) support only a single protocol---the one they were trained on---or (2) require labor-intensive prompting to specify the desired segmentation. We introduce _Pancakes_, a framework that, given a new image from a previously unseen domain, automatically generates multi-label segmentation maps for _multiple_ plausible protocols, while maintaining semantic consistency across related images. In extensive experiments across seven previously unseen domains, _Pancakes_ consistently outperforms strong baselines, often by a wide margin, demonstrating its ability to produce diverse yet coherent segmentation maps on unseen domains.

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination of lexical and semantic scores, as well as the reciprocal rank fusion (RRF) method, and identify their advantages and potential pitfalls. Contrary to existing studies, we find RRF to be sensitive to its parameters; that the learning of a convex combination fusion is generally agnostic to the choice of score normalization; that convex combination outperforms RRF in in-domain and out-of-domain settings; and finally, that convex combination is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain.