A Dual-Task Mutual Learning Framework for Predicting Post-thrombectomy Cerebral Hemorrhage

doi:10.1007/978-3-031-73281-2_6

	A Dual-Task Mutual Learning Framework for Predicting Post-thrombectomy Cerebral Hemorrhage
	Jiang, Caiwen1,2 ; Wang, Tianyu 4; Xing, Xiaodan 2; Liu, Mianxin 6; Yang, Guang 2,7,8,9; Ding, Zhongxiang 4; Shen, Dinggang1,3,5
	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	15187 LNCS
页码	58-68
DOI	10.1007/978-3-031-73281-2_6
摘要	Ischemic stroke is a severe condition caused by the blockage of brain blood vessels, and can lead to the death of brain tissue due to oxygen deprivation. Thrombectomy has become a common treatment choice for ischemic stroke due to its immediate effectiveness. But, it carries the risk of postoperative cerebral hemorrhage. Clinically, multiple CT scans within 0–72 h post-surgery are used to monitor for hemorrhage. However, this approach exposes radiation dose to patients, and may delay the detection of cerebral hemorrhage. To address this dilemma, we propose a novel prediction framework for measuring postoperative cerebral hemorrhage using only the patient’s initial CT scan. Specifically, we introduce a dual-task mutual learning framework to takes the initial CT scan as input and simultaneously estimates both the follow-up CT scan and prognostic label to predict the occurrence of postoperative cerebral hemorrhage. Our proposed framework incorporates two attention mechanisms, i.e., self-attention and interactive attention. Specifically, the self-attention mechanism allows the model to focus more on high-density areas in the image, which are critical for diagnosis (i.e., potential hemorrhage areas). The interactive attention mechanism further models the dependencies between the interrelated generation and classification tasks, enabling both tasks to perform better than the case when conducted individually. Validated on clinical data, our method can generate follow-up CT scans better than state-of-the-art methods, and achieves an accuracy of 86.37% in predicting follow-up prognostic labels. Thus, our work thus contributes to the timely screening of post-thrombectomy cerebral hemorrhage, and could significantly reform the clinical process of thrombectomy and other similar operations related to stroke. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
关键词	Arthroplasty Diagnosis Attention mechanisms Cerebral hemorrhage Dual-task mutual learning Dual-tasks Haemorrage Interactive attention Mutual learning Postoperative cerebral hemorrhage Prediction of hemorrhage progression Thrombectomy
会议名称	9th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2024, 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 10, 2024 - October 10, 2024
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20244317263974
EI主题词	Diseases
EISSN	1611-3349
EI分类号	102.1 ; 102.1.2 ; 102.1.2.1
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/442546
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_博士生
通讯作者	Ding, Zhongxiang; 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 United Imaging Intelligence Co., Ltd., Shanghai, China 4.Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou, China 5.Shanghai Clinical Research and Trial Center, Shanghai; 201210, China 6.Shanghai Artificial Intelligence Laboratory, Shanghai; 200232, China 7.National Heart and Lung Institute, Imperial College London, London, United Kingdom 8.Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom 9.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,Wang, Tianyu,Xing, Xiaodan,et al. A Dual-Task Mutual Learning Framework for Predicting Post-thrombectomy Cerebral Hemorrhage[C]:Springer Science and Business Media Deutschland GmbH,2025:58-68.