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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords