Deep Learning-Based Detection and Diagnosis of Subarachnoid Hemorrhage.

Abstract

Subarachnoid hemorrhage (SAH) is one of the critical and severe neurological diseases with high morbidity and mortality. Head computed tomography (CT) is among the preferred methods for the diagnosis of SAH, which is confirmed by CT showing high-density shadow in the subarachnoid space. Analysis of these images through a deep learning-based subarachnoid hemorrhage will reduce the approximate rate of misdiagnosis in general and missed diagnosis by clinicians in particular. Deep learning-based detection of subarachnoid hemorrhage mainly includes two tasks, i.e., subarachnoid hemorrhage classification and subarachnoid hemorrhage region segmentation. However, it is difficult to effectively judge reliability of the model and classify bleeding which is based on limited predictive probability of convolutional neural network output. Moreover, deep learning-based bleeding area segmentation requires a large amount of training data to be marked in advance and the large number of network parameters makes the model training unable to reach the optimal. To resolve these problems associated with existing models, Bayesian deep learning and neural network-based hybrid model is presented in this paper to estimate uncertainty and efficiently classify subarachnoid hemorrhage. Uncertainty estimation of the proposed model helps in judging whether the model's prediction is reliable or not. Additionally, it is used to guide clinicians to find the neglected subarachnoid hemorrhage area. In addition, a teacher-student mechanism deep learning model was designed to introduce observational uncertainty estimation for semisupervised learning of subarachnoid hemorrhage. Observation uncertainty estimation detects the uncertain bleeding areas in CT images and then selects areas with high reliability. Finally, it uses these unlabeled data for model training purposes as well.