用分割预训练减少脊柱退变分级的标注需求，20%标签即接近全监督效果。
Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading
- 先用伪标签预训练3D ResNet分割椎体、椎间盘和脊柱管
- 仅需20%人工标注标签，平均性能逼近全量标注模型
- 对罕见或空间定位关键的病灶提升显著，适合标注稀缺场景
基于MRI的腰椎退行性病变自动评估需要大规模专家标注数据。而分割伪标签可通过自动化工具低成本生成。本文研究分割预训练能否替代部分人工标注。我们使用3D ResNet编码器在分割任务上预训练，目标是分割椎体、椎间盘（IVDs）和脊柱管，随后在包含约2000名受试者、11种病理的多中心数据集上，用不同比例（10%至100%）的标注数据微调轻量级分类头。预训练模型在伪标签上的Dice分数达0.94。无论在何种标注比例下，任务平均（宏平均）一 vs 其余ROC-AUC均得到提升。仅使用20%标注标签时，性能已接近全监督水平，尤其在低发生率或空间定位重要的病理中表现更优。
原文摘要 · Abstract (English)
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.
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