Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles

Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles

Background and purposeMultiple 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 asses...

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Bibliographic Details
Main 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
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-12-01
Series:Frontiers in Neurology
Subjects:
deep-learning algorithm
intracranial hemorrhage (ICH)
weighted ensemble model
strongly annotated dataset
neuroimaging
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2023.1321964/full

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https://www.frontiersin.org/articles/10.3389/fneur.2023.1321964/full

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