VIDS-Seg：面向儿科心脏超声分割的可靠不确定性量化

中文摘要 AI 辅助

机器学习的临床可靠部署需要模型能够识别自身可能失效的时刻，尤其是针对训练数据中代表性不足的亚群。儿科护理就是一个常见案例，基于成人队列训练的模型可能会在儿童群体中表现不佳，却不会给出任何出错提示。由于用带标签的儿科数据进行重新训练通常不可行，因此在推理阶段检测此类失效是一项关键的临床需求。我们在VIDS（分布偏移下的变分推理）框架的基础上，提出了VIDS-Seg，该方法在轻量级预测头上应用摊销变分推理，使这种自适应的、分布外（OOD）感知先验对于密集图像分割而言是可处理的。我们在超声心动图的左心室分割任务上对VIDS-Seg进行评估，该场景下儿科解剖结构与大多数分割模型所训练的成人群体存在系统性差异；我们在成人群体队列（EchoNet-Dynamic）上进行训练，并在儿科队列（EchoNet-Pediatric）上进行零样本评估。在所有年龄层中，VIDS-Seg在分割精度上与具有竞争力的基线模型相当，同时生成的预测不确定性与分割误差之间的空间对应关系显著更高，即使在对所有基线模型应用温度缩放后，这一优势仍然存在。在下游任务中，它能产生更准确、更稳定的射血分数估计值，以及对婴儿亚群中心脏功能障碍更可靠的检测。我们的结果表明，分布外感知的不确定性量化可以作为部署的分割模型的实用安全层，无需重新训练或额外带标签数据即可检测代表性不足亚群中的隐性失效。

英文摘要

Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data. A common case is pediatric care, where models trained on adult cohorts can silently under-perform on children with no indication that something has gone wrong. As retraining with labeled pediatric data is often infeasible, detecting such failures at inference time is a critical clinical need. Building on the VIDS (Variational Inference under Distribution Shifts) framework, we introduce VIDS-Seg, which applies amortized variational inference over a lightweight prediction head to make this adaptive, OOD-aware prior tractable for dense image segmentation. We evaluate VIDS-Seg on left ventricular segmentation in echocardiography, a setting where pediatric anatomy differs systematically from the adult population most segmentation models are trained on, training on an adult cohort (EchoNet-Dynamic) and evaluating zero-shot on a pediatric cohort (EchoNet-Pediatric). Across all age strata, VIDS-Seg matches competitive baselines in segmentation accuracy while producing substantially higher spatial correspondence between predicted uncertainty and segmentation error, an advantage that persists even after applying temperature scaling to all baselines. Downstream, it yields more accurate and stable ejection fraction estimates and more reliable detection of cardiac malfunction in the infant subgroup. Our results indicate that OOD-aware uncertainty quantification can serve as a practical safety layer for deployed segmentation models, enabling detection of silent failures in underrepresented subgroups without retraining or additional labeled data.

发表机构

• University of Basel（巴塞尔大学）

机构由 AI 辅助整理，请以论文原文为准。