MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality

• URL: http://arxiv.org/abs/2408.09064v1
• Date: Sat, 17 Aug 2024 01:40:00 GMT
• Title: MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality
• Authors: Zhiyi Shi, Junsik Kim, Wanhua Li, Yicong Li, Hanspeter Pfister,
• Abstract summary: We introduce Modality-aware Low-Rank Adaptation (MoRA) for multi-modal pre-trained models. MoRA integrates into the first block of the model, significantly improving performance when a modality is missing. It requires less than 1.6% of the trainable parameters needed compared to training the entire model.
• Score: 29.088860237497165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal pre-trained models efficiently extract and fuse features from different modalities with low memory requirements for fine-tuning. Despite this efficiency, their application in disease diagnosis is under-explored. A significant challenge is the frequent occurrence of missing modalities, which impairs performance. Additionally, fine-tuning the entire pre-trained model demands substantial computational resources. To address these issues, we introduce Modality-aware Low-Rank Adaptation (MoRA), a computationally efficient method. MoRA projects each input to a low intrinsic dimension but uses different modality-aware up-projections for modality-specific adaptation in cases of missing modalities. Practically, MoRA integrates into the first block of the model, significantly improving performance when a modality is missing. It requires minimal computational resources, with less than 1.6% of the trainable parameters needed compared to training the entire model. Experimental results show that MoRA outperforms existing techniques in disease diagnosis, demonstrating superior performance, robustness, and training efficiency.

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