[论文] A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Ta...

论文概要

研究领域: CV 作者: Reza Rajabli, D. Louis Collins 发布时间: 2026-09-04 arXiv: 2609.05400

中文摘要

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原文摘要

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 ...

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