用线性探针评估医学视觉大模型预测疾病进展能力。
Downstream Analysis of Foundational Medical Vision Models for Disease Progression
- 用线性探针分析分割与配准模型的中间层特征。
- 配准模型特征可不依赖空间对齐输入，分割模型则需对齐。
- 发现配准特征更适合捕捉疾病动态变化，适合临床追踪研究。
医学视觉基础模型广泛应用于医学图像分割和配准等任务。本文通过简单线性探针评估这些模型预测疾病进展的能力。假设分割模型的中间层特征捕捉结构信息，而配准模型的特征编码时间变化知识。结果表明，两类特征均有助于疾病进展预测；其中，配准模型特征无需空间对齐输入即可有效工作，而分割模型性能依赖输入图像的空间对齐。研究凸显了空间对齐的重要性，并验证了基础模型特征在图像配准中的价值。
原文摘要 · Abstract (English)
Medical vision foundational models are used for a wide variety of tasks, including medical image segmentation and registration. This work evaluates the ability of these models to predict disease progression using a simple linear probe. We hypothesize that intermediate layer features of segmentation models capture structural information, while those of registration models encode knowledge of change over time. Beyond demonstrating that these features are useful for disease progression prediction, we also show that registration model features do not require spatially aligned input images. However, for segmentation models, spatial alignment is essential for optimal performance. Our findings highlight the importance of spatial alignment and the utility of foundation model features for image registration.
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