Automated detection of intracranial hemorrhage in noncontrast head computed tomography

Intracranial hemorrhages (ICHs) represent a critical medical event associated with poor outcome despite the best of care. Since early recognition and management of ICH can improve the patient outcomes, there is a need for a triaging system to rapidly detect such conditions and expedite the treatment process. This chapter examines the state-of-the-art traditional and deep learning models for automated detection tasks with a focus on ICH. The prior works are summarized to display their respective strengths and weaknesses. This is followed by depicting how the advantages of different types of models can be combined to achieve improved results. The chapter concludes with the proposed roles of such a detection framework in clinical workflow.