EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping

Sam Gijsen; Kerstin Ritter

ICML2025顶会

EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping

Sam Gijsen, Kerstin Ritter

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Multimodal language modeling has enabled breakthroughs for representation learning, yet remains unexplored in the realm of functional brain data for clinical phenotyping. This paper pioneers

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引用它的顶会 Paper3

LLM-Guided Diagnostic Evidence Alignment for Medical Vision–Language Pretraining under Limited PairingHuimin Yan, Liang Bai, Xian Yang, Long ChenICML 2026 · 1 次引用
CerebraGloss: Instruction-Tuning a Large Vision-Language Model for Fine-Grained Clinical EEG InterpretationWei Gu, Tianming Luo, Qiran Zhang, Mohan Ye 等ICLR 2026
KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural InterpretationHaoning Wang, Wenchao Yang, Shuai Shen, Yang LiICML 2026

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VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised LearningAdrien Bardes, Jean Ponce, Yann LeCunICLR 2022 · 1,226 次引用

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