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Abstract #0992

Meta-Learning-Driven Few-Shot Contrastive Learning for Stroke Prognosis Prediction across Multimodal Datasets

Haoran Peng1, Rencheng Zheng1, Yajing Zhang2, Chengyan Wang3, and He Wang1
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2GE Healthcare, Beijing, China, Shanghai, China, 3Human Phenome Institute and Shanghai Pudong Hospital, Fudan University, Shanghai, China, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Diagnosis/Prediction, Contrastive Learning, Few-shot learning

Motivation: The challenge of poor generalization performance with small sample sizes in stroke prognosis prediction tasks, especially due to difficulties in collecting follow-up data.

Goal(s): To develop a framework that effectively utilizes small yet related datasets for stroke prognosis prediction, improving generalization and performance on limited data.

Approach: The proposed approach is a few-shot contrastive learning framework that integrates a two-step meta-learning training paradigm, capturing domain-specific prior knowledge using both structured and unstructured data.

Results: Evaluations on two stroke datasets (341 and 309 patients) show that the proposed framework outperforms SimCLR and traditional supervised methods, demonstrating improved resilience with reduced data availability.

Impact: This research advances stroke recovery prediction by enhancing model robustness and generalization with limited data. The framework's ability to integrate diverse datasets could improve clinical decision-making in stroke rehabilitation, addressing a critical gap for accurate predictive modeling in this domain.

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