Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Abstract Background TOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architec...

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Bibliographic Details
Main Authors: Shuo Zhang, Jing Wang, Lulu Pei, Kai Liu, Yuan Gao, Hui Fang, Rui Zhang, Lu Zhao, Shilei Sun, Jun Wu, Bo Song, Honghua Dai, Runzhi Li, Yuming Xu
Format: Article
Language:English
Published: BMC 2022-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Interpretability
Ischemic Stroke
Active learning
Classification algorithm
Loss function
Online Access:https://doi.org/10.1186/s12911-021-01721-5

Internet

https://doi.org/10.1186/s12911-021-01721-5

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