Yunan Wu; Mark P. Supanich; Deng Jie

doi:https://doi.org/10.2991/jaims.d.210618.001

Authors

Yunan Wu Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Street 181, Chicago, IL, 60612, USA https://orcid.org/0000-0001-6980-9746
Mark P. Supanich 1Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Street 181, Chicago, IL, 60612, USA https://orcid.org/0000-0001-9514-9390
Deng Jie Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Street 181, Chicago, IL, 60612, USA

DOI:

https://doi.org/10.2991/jaims.d.210618.001

Keywords:

Intracranial hemorrhage, Subtype classification, Computer tomography, Deep learning, Ensembled model

Abstract

Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure timely treatment. In this study, we developed an ensembled deep neural network for the detection and subtype classification of intracranial hemorrhage. The model consisted of two parallel network pathways, one using three different window level/width settings to enhance the image contrast of brain, blood, and soft tissue. The other extracted spatial information of adjacent image slices to the target slice. Both pathways exploited the EfficientNet-B0 as the basic architecture and were ensembled to generate the final prediction. Class activation mapping was applied in both pathways to highlight the regions of detected hemorrhage and the associated subtypes. The model was trained and tested using Intracranial Hemorrhage Detection Challenge (IHDC) dataset launched by the Radiological Society of North America (RSNA) in 2019, which contained 674,258 head noncontrasts computer tomography images acquired from 19,530 patients. An independent dataset (CQ500) acquired from another institution was used to test the generalizability of the trained model. The overall accuracy, sensitivity, and F1 score for intracranial hemorrhage detection were 95.7%, 85.9%, and 86.7% on IHDC testing dataset and 92.4%, 92.6%, and 93.4% on external CQ500 testing dataset. The heatmaps by class activation mapping successfully demonstrated discriminative feature regions of the predicted hemorrhage locations and subtypes, providing visual guidance for radiologists to assist in rapid diagnosis of intracranial hemorrhage.

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Ensembled Deep Neural Network for Intracranial Hemorrhage Detection and Subtype Classification on Noncontrast CT Images

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