Multi-Task Learning for Pulmonary Arterial Hypertension Prognosis Prediction Via Memory Drift and Prior Prompt Learning on 3D Chest CT

Author:

Yang Guanyu1ORCID,He Yuting1ORCID,Lv Yang1,Chen Yang1ORCID,Coatrieux Jean-Louis2ORCID,Sun Xiaoxuan3,Wang Qiang3,Wei Yongyue4,Li Shuo5ORCID,Zhu Yinsu6

Affiliation:

1. LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China

2. Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Nanjing, China

3. Department of Rheumatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China

4. Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China

5. Department of Biomedical Engineering and the Department of Computer and Data Science, Case Western Reserve University, Cleveland, OH, USA

6. Department of Radiology, Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China

Funder

Intergovernmental Cooperation Project of the National Key Research and Development Program of China

CAAI-Huawei MindSpore Open Fund and Scientific Research Foundation of Graduate School of Southeast University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Reference49 articles.

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2. Learning genomic representations to predict clinical outcomes in cancer;yousefi;Proc Int Conf Learn Representation Workshop (ICLRW),0

3. Extracting and composing robust features with denoising autoencoders

4. A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets

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