Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer's Disease

• URL: http://arxiv.org/abs/2411.07871v1
• Date: Tue, 12 Nov 2024 15:28:06 GMT
• Title: Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer's Disease
• Authors: Francesco Chiumento, Mingming Liu,
• Abstract summary: This paper generates synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports.
• Score: 0.7696359453385685
• License:
• Abstract: The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheimer's disease, due to the lack of comprehensive diagnostic reports that can be utilized for model fine-tuning. This paper addresses this gap by generating synthetic diagnostic reports using GPT-4o-mini on structured data from the OASIS-4 dataset, which comprises 663 patients. Using the synthetic reports as ground truth for training and validation, we then generated neurological reports directly from the images in the dataset leveraging the pre-trained BiomedCLIP and T5 models. Our proposed method achieved a BLEU-4 score of 0.1827, ROUGE-L score of 0.3719, and METEOR score of 0.4163, revealing its potential in generating clinically relevant and accurate diagnostic reports.

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