MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning

arXiv:2312.14574v2 Announce Type: replace-cross
Abstract: Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network …

abstract analysis arxiv cs.cv cs.lg data data analysis diagnosis fine-tuning graph large models medical medical data multimodal prompt prompt learning replace tasks tuning type