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Found 3 papers, 1 papers with code
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications
Our paper investigates the efficiency and effectiveness of using vector embeddings from single-modal foundation models and multi-modal Vision-Language Models (VLMs) for multimodal deep learning in low-resource environments, particularly in healthcare.
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare.
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss.
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