2025-03-24 レンセラー工科大学(RPI)
<関連情報>
- https://news.rpi.edu/2025/03/24/multimodal-multitask-foundation-model-enhances-lung-cancer-screening-and-beyond
- https://www.nature.com/articles/s41467-025-56822-w
肺がん検診のための医療用マルチモーダル・マルチタスク基盤モデル Medical multimodal multitask foundation model for lung cancer screening
Chuang Niu,Qing Lyu,Christopher D. Carothers,Parisa Kaviani,Josh Tan,Pingkun Yan,Mannudeep K. Kalra,Christopher T. Whitlow &Ge Wang
Nature Communications Published:11 February 2025
DOI:https://doi.org/10.1038/s41467-025-56822-w
Abstract
Lung cancer screening (LCS) reduces mortality and involves vast multimodal data such as text, tables, and images. Fully mining such big data requires multitasking; otherwise, occult but important features may be overlooked, adversely affecting clinical management and healthcare quality. Here we propose a medical multimodal-multitask foundation model (M3FM) for three-dimensional low-dose computed tomography (CT) LCS. After curating a multimodal multitask dataset of 49 clinical data types, 163,725 chest CT series, and 17 tasks involved in LCS, we develop a scalable multimodal question-answering model architecture for synergistic multimodal multitasking. M3FM consistently outperforms the state-of-the-art models, improving lung cancer risk and cardiovascular disease mortality risk prediction by up to 20% and 10% respectively. M3FM processes multiscale high-dimensional images, handles various combinations of multimodal data, identifies informative data elements, and adapts to out-of-distribution tasks with minimal data. In this work, we show that M3FM advances various LCS tasks through large-scale multimodal and multitask learning.
