适用于阿尔茨海默病相关脑部MRI任务的通用特征提取器
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
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中文总结 AI 辅助
该研究将用于脑年龄预测的3D CNN冻结权重，用LoRA以约1%额外参数适配，在六项阿尔茨海默病相关MRI任务中验证了其作为通用基础模型的有效性，可跨数据集迁移且表现优异。
中文摘要 AI 辅助
当没有足够的标注数据来正确训练深度学习模型时，迁移学习可以提供帮助。但我们仍未完全理解其在神经成像领域的有效性，尤其是在阿尔茨海默病研究中；此外，这些迁移后的模型是否能在不针对每个特定任务重新训练的情况下应用于新数据集也尚不明确。我们评估了一个紧凑的有监督预训练模型是否可作为下游神经成像任务的可复用基础模型。我们冻结了一个此前用于脑年龄预测的3D CNN的718万个权重，并使用低秩适配（LoRA）将其适配到每个任务，仅需要约1%的额外可训练参数。我们通过六个实验评估其通用性：实验1中，将模型适配为在ADNI数据集上区分认知正常与痴呆，在保留的折叠上获得了0.964的AUC；实验2中，将该适配后的模型不做任何重新训练直接应用于OASIS-3数据集，获得了0.871的AUC；实验3中，复用其输出的logit并结合年龄和认知评分，区分稳定型与进展型轻度认知障碍（MCI），获得了0.828的AUC；实验4中，将同一主干网络适配为从结构MRI预测淀粉样蛋白阳性，获得了0.804的AUC；实验5和6中，采用相同方法直接从T1加权图像估计经颅内体积（ICV）归一化的海马体及白质低信号体积，R²分别为0.80和0.91，而这类任务通常需要使用大得多的U-Net网络来完成。因此，在脑年龄任务上进行有监督训练的紧凑模型可作为可复用的主干网络，仅需约1%的额外参数即可适配至各任务，且无需任何训练即可迁移至未见过的队列。我们的研究表明，经过精心训练的脑年龄模型可作为阿尔茨海默病相关任务的有效基础模型，即使在严格的数据约束下也能发挥作用。
英文摘要
When there is not enough labeled data to properly train deep learning models, transfer learning can help. We still do not fully understand how effective it is in neuroimaging, especially for Alzheimer's disease research. It is also not clear if these transferred models can work on new datasets without being retrained for each specific task. We evaluate whether a compact, supervised pretrained model can serve as a reusable foundation model for downstream neuroimaging tasks. We freeze the 7.18 million weights of a 3D CNN previously trained for brain-age prediction, and adapt it to each task using Low-Rank Adaptation (LoRA), requiring only ~1% additional trainable parameters. We evaluate generalizability in six experiments. Adapting the model to classify cognitively normal versus Dementia on ADNI gave an AUC of 0.964 on held-out folds (Experiment #1). Applying that adapted model unchanged to OASIS-3, with no retraining, gave an AUC of 0.871 (Experiment #2). Reusing its output logit together with age and a cognitive score distinguished stable from progressing MCI with an AUC of 0.828 (Experiment #3). Adapting the same backbone to predict amyloid positivity from structural MRI gave an AUC of 0.804 (Experiment #4). Finally, the same approach estimated ICV-normalized hippocampal and white matter hypointensity volumes directly from the T1w image, with R^2 of 0.80 and 0.91 respectively, tasks normally addressed with much larger U-Net networks (Experiments #5 and #6). A compact model supervised on brain age can therefore serve as a reusable backbone, adapting to each task with ~1% additional parameters and transferring to an unseen cohort without any training. Our findings suggest that a carefully trained brain age model can serve as an effective foundation model for Alzheimer's related tasks, even under strict data constraints.
发表机构
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）
- Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
机构由 AI 辅助整理，请以论文原文为准。
