Continual learning framework for a multicenter study with an application to electrocardiogram
Abstract Deep learning has been increasingly utilized in the medical field and achieved many goals. Since the size of data dominates the performance of deep learning, several medical institutions are conducting joint research to obtain as much data as possible. However, sharing data is usually prohi...
Main Authors: | Junmo Kim, Min Hyuk Lim, Kwangsoo Kim, Hyung-Jin Yoon |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2024-03-01
|
Series: | BMC Medical Informatics and Decision Making |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12911-024-02464-9 |
Similar Items
-
Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre studyResearch in context
by: Junmo Kim, et al.
Published: (2024-02-01) -
Prediction Models for Glaucoma in a Multicenter Electronic Health Records Consortium: The Sight Outcomes Research Collaborative
by: Sophia Y. Wang, MD, MS, et al.
Published: (2024-05-01) -
Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias
by: ZengDing Liu, et al.
Published: (2020-09-01) -
An Experimental Survey of Incremental Transfer Learning for Multicenter Collaboration
by: Yixing Huang, et al.
Published: (2024-01-01) -
An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals
by: Xiaomao Fan, et al.
Published: (2021-02-01)