Few-Shot Contrastive Learning for Cross-Task Stroke Prognosis Prediction With Multimodal Data

Predicting stroke outcome remains challenging due to inherent heterogeneity, misalignment of multimodal clinical data, and the availability of well-annotated longitudinal datasets. Current methodologies often lack robustness and generalizability across these tasks. We propose a few-shot contrastive learning framework that integrates brain MRI images and structured clinical records for cross-task prognosis prediction, addressing both morphological and functional outcomes. Our method combines Model-Agnostic Meta-Learning (MAML) with a two-step contrastive learning strategy including self-awareness learning that captures task-specific features and domain learning that facilitates cross-dataset generalization. To handle inconsistencies in tabular data, a Misalignment Separation technique was adopted. The framework jointly trains a domain encoder on multimodal inputs, capturing shared and task-specific prior knowledge to enhance predictive robustness. Evaluations on 309 patients for morphological outcome and 341 patients for functional outcome, as well as on external validation datasets, demonstrated that our approach outperformed SimCLR and conventional supervised methods, and could effectively integrate cross-task datasets. This framework highlights the potential of multimodal few-shot learning for robust stroke prognosis prediction for small-sample datasets.