Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation

TL;DR - An arXiv preprint proposing a Bayesian adaptively-weighted ensemble that combines multiple few-shot segmentation algorithms for abdominal/pelvic anatomy, tuning ensemble weights per target domain. It matters because it addresses the practical bottleneck of deploying segmentation models at new clinical sites with scarce labels and institutional domain shift.

• Multiple few-shot segmentation algorithms are first adapted on a small labelled support set; Bayesian optimisation then searches ensemble weights that maximise performance on a target-domain validation set.
• Learned weights are frozen and applied to unseen query images from the same target domain, unlike fixed-weight ensembles that cannot adapt model contributions across anatomies or institutions.
• Evaluated on the Cross-institution Male Pelvic Structures dataset using held-out anatomical structures and held-out institutions to jointly simulate label scarcity and domain shift.
• Reported statistically significant gains over individual few-shot learners, fixed-weight ensembles, train-from-scratch baselines, and recent state-of-the-art ensembling methods (no numeric results given in the provided abstract).