Summary
Researchers Reza Rajabli and D. Louis Collins investigate whether a compact, supervised pretrained model can serve as a reusable foundation model for neuroimaging, addressing the shortage of labeled data in Alzheimer's disease research. The authors freeze the 7.18 million weights of a 3D CNN previously trained for brain-age prediction and adapt it to downstream tasks using Low-Rank Adaptation (LoRA), requiring only about 1% additional trainable parameters. Generalizability is assessed across six experiments, including classifying cognitively normal individuals versus Dementia patients. The paper, published on arXiv (2609.05400) in the computer vision field, examines how effective transfer learning is in neuroimaging and whether such transferred models can perform well on new datasets without task-specific retraining. This work offers a parameter-efficient approach for brain MRI analysis.
Paper Overview
Field: Computer Vision (CV)
Authors: Reza Rajabli, D. Louis Collins
Published: 2026-09-04
arXiv: 2609.05400
Abstract (Translated)
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...
*(Abstract truncated at source; see the arXiv page for the full text.)*
*Auto-collected on 2026-09-08.*
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