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 model. Results To evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4. Conclusions We construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

Stroke classification and treatment support system artificial intelligence for usefulness of stroke diagnosis

“…The corresponding precision rates obtained were 70.3% for cardioembolic stroke, 65.3% for large artery atherosclerosis (LAA), 62.3% for small vessel occlusion (SVO), and 73.7% for cryptogenic stroke. Additionally, Zhang et al ( 35 ) performed similar analyzes, resulting in precision rates of 53.3% for cardioembolic stroke, 74.5% for LAA, 54.7% for SVO, and 20.0% for cryptogenic stroke. Moreover, Wang et al ( 36 ) conducted an analysis excluding cryptogenic stroke, achieving precision rates of 94.07% for cardioembolic stroke, 76.73% for LAA, and 72.13% for SVO.…”

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