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 architecture to classify TOAST.

Methods

To simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the mode

SUBMITTER: Zhang S 

PROVIDER: S-EPMC8729146 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature