用蒸馏模型提升低场新生儿脑部MRI伪影分级准确率
LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MR
- 通过多教师路由与领域内蒸馏，构建专用小模型LoFi RADIO
- 在7类伪影上实现加权综合评分提升，效果优于多模型部署
- 适合医疗影像质量评估、低资源环境下的AI辅助诊断
超低场磁共振成像使新生儿脑部影像在资源匮乏地区得以应用，但其信噪比低、缺乏屏蔽且扫描时间长，极易产生采集伪影，亟需自动化质量控制。本文针对LISA 2026任务1a：对超低场T2加权图像中的七类常见伪影进行多标签严重度分级（0/1/2）。我们发现多个骨干网络可与分类MLP有效结合，但无单一骨干在所有伪影上表现最优。为提升性能，评估了基于伪影类型的教师路由策略，并在无标注低场MRI语料库上将多个互补的预训练模型蒸馏为一个领域内ViT-S学生模型（LoFi RADIO）。两种策略均提升了加权综合评分。蒸馏后的骨干模型性能匹配甚至超过多教师路由方案，且推理时无需部署多个大型基础模型，更具实用性。
原文摘要 · Abstract (English)
Ultra-low-field MRI makes neonatal brain imaging deploy- able in low-resource settings, but its low SNR, lack of shielding, and long scan duration make it especially prone to acquisition artifacts, motivating automated quality control. We address the LISA 2026 Task 1a challenge: multi-label severity grading (0/1/2) of seven common image artifacts on ULF T2 weighted volumes. We identify that a number of backbones may be successfully paired with a classification MLP, but that no single backbone is uniformly best across artifacts. To improve performance, we evaluate routing complementary foundation model teachers through a per-artifact gate, as well as distilling the teachers into a single in-domain ViT-S student (LoFi RADIO) over an unlabeled low-field MRI corpus. Both of these strategies improve the weighted composite. The distilled backbone matches or exceeds the gate and has the added advantage of not requiring deployment of multiple large foundation models at infer- ence.
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