传统解剖特征与深度学习特征在脑部MRI诊断中表现相当，无需复杂模型。
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
- 对比解剖测量、CNN和ViT三种特征提取方法的性能。
- 解剖特征线性模型达到与复杂AI模型相当的诊断准确率。
- 提出新预训练方法ASP，提升生物年龄预测效果。
本研究系统评估了三种主流AI神经影像特征提取方法：（1）解剖结构表面与体积计算，（2）基于卷积神经网络（CNN）的有监督学习，（3）视觉变换器（ViT）基础模型的无监督预训练后微调。研究基于18个公开数据集，涵盖约8万例参与者的3D结构化T1加权MRI扫描，涉及七项临床任务。结果表明，基于解剖特征的线性模型在诊断性能上可媲美复杂非线性特征，即使后者由在数千张图像上训练的基础模型学习而来。CNN与预训练ViT所学特征隐式包含相关解剖信息，无需显式提取。据此，我们提出解剖分割预训练（ASP）方法，在基础模型预训练中融入解剖信息，显著提升生物年龄估计性能。
原文摘要 · Abstract (English)
In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical surfaces and volumes, (2) supervised learning with convolutional neural networks (CNNs), and (3) unsupervised pretraining of vision transformer (ViT) foundation models, followed by supervised finetuning. Our study is based on 18 publicly available datasets containing 3D structural T1-weighted MRI scans from approximately 80,000 participants across seven distinct clinical tasks. We observe that a linear model based on anatomical features matches the diagnostic performance of complex nonlinear features learned by sophisticated AI frameworks, including foundation models trained on thousands of scans. Conversely, CNNs and pretrained ViTs learn features that implicitly capture relevant anatomical information, bypassing the need for explicit feature extraction. Building upon these insights, we propose Anatomy Segmentation Pretraining (ASP), a novel method to incorporate anatomical information during foundation-model pretraining, which outperforms existing models in biological age estimation.
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