研究脑影像数据质量对预训练模型的影响，发现低质数据会拖累年龄预测效果。
Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction
- 用不同质量的脑影像数据做预训练，评估其对下游任务的影响。
- 低质量数据组在年龄预测上表现差15%以上，高质数据提升显著。
- 提醒临床领域需针对性筛选数据，避免盲目使用杂乱影像预训练。
大规模脑影像数据为通过预训练构建领域基础模型提供了前所未有的机会。然而，与计算机视觉中的自然图像数据集不同，这些神经影像数据常表现出高度异质性，从结构良好的扫描到严重失真或不完整的脑体积均有存在。这引发了一个根本问题：噪声或低质量扫描能否对预训练产生有意义贡献，还是反而阻碍模型学习？本研究系统探讨了数据质量水平在预训练中的作用及其对下游任务的影响。具体而言，我们在不同质量水平的数据集上进行预训练，并在外部队列上微调用于脑龄预测。结果表明，不同质量水平间性能差异显著，揭示了机遇与局限并存。我们进一步讨论了计算机视觉实践与临床神经影像标准之间的差距，强调了领域感知数据清洗对构建可信且可泛化的专用领域基础模型的重要性。
原文摘要 · Abstract (English)
Large-scale brain imaging datasets provide unprecedented opportunities for developing domain foundation models through pretraining. However, unlike natural image datasets in computer vision, these neuroimaging data often exhibit high heterogeneity in quality, ranging from well-structured scans to severely distorted or incomplete brain volumes. This raises a fundamental question: can noise or low-quality scans contribute meaningfully to pretraining, or do they instead hinder model learning? In this study, we systematically explore the role of data quality level in pretraining and its impact on downstream tasks. Specifically, we perform pretraining on datasets with different quality levels and perform fine-tuning for brain age prediction on external cohorts. Our results show significant performance differences across quality levels, revealing both opportunities and limitations. We further discuss the gap between computer vision practices and clinical neuroimaging standards, emphasizing the necessity of domain-aware curation to ensure trusted and generalizable domain-specific foundation models.
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