用于小样本腹部分割的贝叶斯自适应加权集成方法
Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation
- School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）
- Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对标注稀缺与域偏移问题，提出贝叶斯自适应加权集成框架，在跨机构盆腔结构数据集上显著优于现有方法，可用于部署临床分割系统
AI中文摘要：
小样本学习已成为标注数据稀缺时解剖结构分割的有前景方法。然而，不同小样本学习算法各有优劣，性能随解剖目标和机构变化。现有结合多算法预测的小样本分割集成通常采用固定加权方案，无法根据目标域调整模型贡献。本研究提出一种用于标注稀缺和域偏移下分割的贝叶斯自适应加权集成框架：首先用带标注的小支持集适配多个小样本分割算法；随后用贝叶斯优化自动识别集成权重，以最大化目标域验证集上的分割性能；学习到的权重被固定，用于组合目标域新查询图像的预测。在跨机构男性盆腔结构数据集上，保留解剖结构和机构以模拟标注稀缺和机构域偏移，评估该框架。结果显示，其相比单个小样本学习者、固定权重集成、从头训练基线及近期最先进集成方法，有统计学显著提升。通过调整模型对目标解剖结构和机构域的贡献，该框架为在严重标注约束下将分割系统部署到新临床站点提供了实用机制。
英文摘要：
Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning algorithms exhibit complementary strengths and weaknesses, with performance varying across anatomical targets and institutions. Existing few-shot segmentation ensembles, that combine predictions from multiple algorithms, typically employ fixed weighting schemes and therefore cannot adjust model contributions according to the target domain. In this work, we propose a Bayesian adaptively-weighted ensemble framework for segmentation under label scarcity and domain shift. Multiple few-shot segmentation algorithms are first adapted using a small labelled support set. Bayesian optimisation is then used to automatically identify ensemble weights that maximise segmentation performance on a target-domain validation set. The learned weights are subsequently fixed and applied to combine predictions on previously unseen query images from the target domain. The proposed framework is evaluated on the Cross-institution Male Pelvic Structures dataset using held-out anatomical structures and institutions to simulate simultaneous label scarcity and institutional domain shift. Results demonstrate statistically significant improvements over individual few-shot learners, fixed-weight ensembles, training-from-scratch baselines and recent state-of-the-art ensembling approaches. By adapting model contributions to the target anatomy and institutional domain, the proposed framework provides a practical mechanism for deploying segmentation systems to new clinical sites under severe annotation constraints.
