发表机构
ETH Zurich; Swiss Institute of Bioinformatics (SIB); University of Oxford（苏黎世联邦理工学院; 瑞士生物信息学研究所; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探究用分割预训练替代部分手动分级标注，在约2000名受试者的多中心腰椎MRI数据集上，该方法仅用20%分级标签即可接近全监督性能，提升了腰椎退变分级效果。
AI 中文摘要
对磁共振成像（MRI）中腰椎退变病理的自动评估需要获取专家标注放射学分级的大规模数据集，而分割伪标签可通过自动化工具生成，放射科医师成本极低。本研究探究基于分割的预训练是否能有效替代下游监督所需的部分手动分级标注：我们预训练3D ResNet编码器以分割椎体、椎间盘（IVDs）和椎管，随后使用10%至100%不等的可用训练数据微调轻量任务专用分级头。在包含约2000名受试者、11种病理的多中心数据集上，分割预训练（对伪标签的Dice分数达0.94）在所有数据比例下均提升了任务平均（宏）一对多ROC-AUC；预训练后仅用20%的分级标签，该方法就达到接近全监督的性能，对低患病率或空间定位明确的病理提升最为显著。
英文摘要
Automated assessment of degenerative pathology in the lumbar spine on magnetic resonance imaging (MRI) requires access to large-scale datasets of expert-annotated radiological gradings. In contrast, segmentation pseudo-labels can be generated by automated tools at negligible radiologist cost. We examine whether pre-training on segmentation can effectively replace a fraction of the manual grading annotations required for downstream supervision. We pre-train a 3D ResNet encoder to segment the vertebrae, intervertebral discs (IVDs), and the spinal canal, then fine-tune lightweight task-specific grading heads using different proportions of the available training data, ranging from $10\%$ to $100\%$. On a multicentre dataset of ${\sim}2{,}000$ subjects across 11 pathologies, segmentation pre-training, achieving a Dice score of $0.94$ against pseudo-labels, improved the task-averaged (macro) one-vs-rest ROC-AUC at all proportions. With only 20\% of grading labels after pre-training, the method achieved near full-supervision performance, with the largest gains observed for either low-prevalence or spatially grounded pathologies.
CommentsThe 2nd MICCAI Workshop on Efficient Medical AI (2026)
