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

arXiv: https://arxiv.org/abs/2408.09064 · HTML: https://mlsanity.com/paper/2408.09064/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Towards Robust Multimodal Prompting With Missing Modalities](https://mlsanity.com/paper/2312.15890/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [MoRA: Missing Modality Low-Rank Adaptation for Visual Recognition](https://mlsanity.com/paper/2511.06225/) (2025)
- [Robust Multimodal Learning via Cross-Modal Proxy Tokens](https://mlsanity.com/paper/2501.17823/) (2025)
- [M-ArtAgent: Evidence-Based Multimodal Agent for Implicit Art Influence Discovery](https://mlsanity.com/paper/2604.07468/) (2026)

## Abstract (arXiv)

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.