A deep learning model was developed to detect intracranial hemorrhage in computed tomography scans without medical annotation.
A paper presented at the Radiological Society of North America (RSNA) 2021 Annual Meeting describes a deep learning model that was developed to successfully detect intracranial hemorrhage in computed tomography (CT) scans without medical annotation.
“This method is faster because it removes the need for medical annotation, which can be added later on image errors to improve the model,” the authors wrote. “By detecting 96.6% of the normal exams, we can speed up doctor’s work, providing more time for the analysis of the slices with bleeding, increasing the precision of the diagnosis.”
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