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BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

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

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.CV · 计算机视觉 · first seen 2026-09-10

Abstract

fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates diffic

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#98 most recent of 237 cs.CV papers we have recorded · ↑ newer: Precision in Rice Variety Classification using Stacking-Based Ensemble · ↓ older: DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning
Cite this page: BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models: the #98 most recent of 237 cs.CV papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/braintaskonomy-learning-how-to-pretrain-and-what-to-transfer-in-fmri-foundation-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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