Data-Efficient Fine-Tuning of Vision-Language Models for Diagnosis of Alzheimer's Disease

• URL: http://arxiv.org/abs/2509.07613v3
• Date: Wed, 15 Oct 2025 05:06:34 GMT
• Title: Data-Efficient Fine-Tuning of Vision-Language Models for Diagnosis of Alzheimer's Disease
• Authors: Fangqi Cheng, Surajit Ray, Xiaochen Yang,
• Abstract summary: Medical vision-language models (Med-VLMs) have shown impressive results in tasks such as report generation and visual question answering.<n>Most existing models are typically trained from scratch or fine-tuned on large-scale 2D image-text pairs.<n>We propose a data-efficient fine-tuning pipeline to adapt 3D CT-based Med-VLMs for 3D MRI.
• Score: 3.46857682956989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical vision-language models (Med-VLMs) have shown impressive results in tasks such as report generation and visual question answering, but they still face several limitations. Most notably, they underutilize patient metadata and lack integration of clinical diagnostic knowledge. Moreover, most existing models are typically trained from scratch or fine-tuned on large-scale 2D image-text pairs, requiring extensive computational resources, and their effectiveness on 3D medical imaging is often limited due to the absence of structural information. To address these gaps, we propose a data-efficient fine-tuning pipeline to adapt 3D CT-based Med-VLMs for 3D MRI and demonstrate its application in Alzheimer's disease (AD) diagnosis. Our system introduces two key innovations. First, we convert structured metadata into synthetic reports, enriching textual input for improved image-text alignment. Second, we add an auxiliary token trained to predict the mini-mental state examination (MMSE) score, a widely used clinical measure of cognitive function that correlates with AD severity. This provides additional supervision for fine-tuning. Applying lightweight prompt tuning to both image and text modalities, our approach achieves state-of-the-art performance on ADNI with only 1,504 training MRIs, outperforming methods trained on 27,161 MRIs, and shows strong zero-shot generalization on OASIS-2 and AIBL. Code is available at https://github.com/CFQ666312/DEFT-VLM-AD.

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