CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression

doi:10.1007/978-3-031-73284-3_9

	CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression
	Jiang, Caiwen1,2 ; Xing, Xiaodan 2; Ou, Zaixin1 ; Liu, Mianxin 4; Simon, Walsh 2; Yang, Guang 2,5,7,8; Shen, Dinggang1,3,6
	2025
会议录名称	LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN	0302-9743
卷号	15241 LNCS
页码	83-93
DOI	10.1007/978-3-031-73284-3_9
摘要	The progression of Idiopathic Pulmonary Fibrosis (IPF) significantly correlates with higher patient mortality rates. Early detection of IPF progression is critical for initiating timely treatment, which can effectively slow down the advancement of the disease. However, the current clinical criteria define disease progression requiring two CT scans with a one-year interval, presenting a dilemma: a disease progression is identified only after the disease has already progressed. To this end, in this paper, we develop a novel diffusion model to accurately predict the progression of IPF by generating patient’s follow-up CT scan from the initial CT scan. Specifically, from the clinical prior knowledge, we tailor improvements to the traditional diffusion model and propose a Clinically-Informed Residual Diffusion model, called CIResDiff. The key innovations of CIResDiff include 1) performing the target region pre-registration to align the lung regions of two CT scans at different time points for reducing the generation difficulty, 2) adopting the residual diffusion instead of traditional diffusion to enable the model focus more on differences (i.e., lesions) between the two CT scans rather than the largely identical anatomical content, and 3) designing the clinically-informed process based on CLIP technology to integrate lung function information which is highly relevant to diagnosis into the reverse process for assisting generation. Extensive experiments on clinical data demonstrate that our approach can outperform state-of-the-art methods and effectively predict the progression of IPF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
关键词	Diagnosis Patient treatment Prediction models Pulmonary diseases Clinically informed CLIP-based text processing CT-scan Diffusion model Disease progression Idiopathic pulmonary fibrosis Prediction of pulmonary fibrose progression Pulmonary fibrosis Residual diffusion model Text-processing
会议名称	15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
会议地点	Marrakesh, Morocco
会议日期	October 6, 2024 - October 6, 2024
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20244517332434
EI主题词	Lung cancer
EISSN	1611-3349
EI分类号	102.1 ; 102.1.1 ; 102.1.2.1 ; 1101
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/449153
专题	生物医学工程学院_PI研究组_沈定刚组信息科学与技术学院_硕士生信息科学与技术学院_博士生
通讯作者	Shen, Dinggang
作者单位	1.School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China 2.Bioengineering Department and Imperial-X, Imperial College London, London, United Kingdom 3.Shanghai Clinical Research and Trial Center, Shanghai; 201210, China 4.Shanghai Artificial Intelligence Laboratory, Shanghai; 200232, China 5.National Heart and Lung Institute, Imperial College London, London, United Kingdom 6.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China 7.Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom 8.School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Jiang, Caiwen,Xing, Xiaodan,Ou, Zaixin,et al. CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression[C]:Springer Science and Business Media Deutschland GmbH,2025:83-93.