Strengthening Deep-Learning Models for Intracranial Hemorrhage Detection: Strongly Annotated Computed Tomography Images and Model Ensembles

Authors

Dong-Wan Kang
Gi-Hun Park
Wi-Sun Ryu
Dawid Schellingerhout
Museong Kim
Yong Soo Kim
Chan-Young Park
Keon-Joo Lee
Moon-Ku Han
Han-Gil Jeong
Dong-Eog Kim

Publication Date

1-1-2023

Journal

Frontiers in Neurology

Abstract

BACKGROUND AND PURPOSE: Multiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance.

METHODS: We used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training. DenseNet121, InceptionResNetV2, MobileNetV2, and VGG19 were trained on strongly and weakly annotated datasets and compared using independent external test datasets. We then developed a weighted ensemble model combining separate models trained on all ICH, subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and small-lesion ICH cases. The final weighted ensemble model was compared to four well-known deep-learning models. After external testing, six neurologists reviewed 91 ICH cases difficult for AI and humans.

RESULTS: InceptionResNetV2, MobileNetV2, and VGG19 models outperformed when trained on strongly annotated datasets. A weighted ensemble model combining models trained on SDH, SAH, and small-lesion ICH had a higher AUC, compared with a model trained on all ICH cases only. This model outperformed four deep-learning models (AUC [95% C.I.]: Ensemble model, 0.953[0.938-0.965]; InceptionResNetV2, 0.852[0.828-0.873]; DenseNet121, 0.875[0.852-0.895]; VGG19, 0.796[0.770-0.821]; MobileNetV2, 0.650[0.620-0.680];

CONCLUSION: We propose a weighted ensemble model for ICH detection, trained on large-scale, strongly annotated CT scans, as no model can capture all aspects of complex tasks.

Keywords

deep-learning algorithm, intracranial hemorrhage (ICH), weighted ensemble model, strongly annotated dataset, neuroimaging

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Associated Data

PMID: 38221995