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Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
Christian Gapp, Author 2, Author 3, Author 4, Author 5
multimodalself-supervised learningfine-tuningmedical imaging
2609.22271
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1h ago80%
Abstract
This study investigates the impact of self-supervised pretraining on multimodal stroke recurrence prediction, focusing on improving modality balance and cross-modal integration.
Reality Card
Core Claim
Self-supervised pretraining significantly enhances the utilization of multimodal datasets for stroke recurrence prediction, outperforming baseline and non-pretrained models.
Method / Result
The best-performing Vision Transformer based neural network effectively overcomes unimodal collapse.
Limitations
The effect of modality contributions and cross-modal behavior in multimodal medical models remains largely unexplored.
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