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A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks

Paper recorded by Signals 4 on 2026-09-04 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.CV · 计算机视觉 · first seen 2026-09-07

Abstract

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 mode

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#129 most recent of 237 cs.CV papers we have recorded · ↑ newer: WorldSculpt: Generating Compositional Worlds from Grounded Videos · ↓ older: From Interpretability Methods to Interpretable Models
Cite this page: A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks: the #129 most recent of 237 cs.CV papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-generalizable-feature-extractor-for-alzheimer-s-related-brain-mri-tasks.html
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