Automated detection of intracranial hemorrhage in noncontrast head computed tomography

Autor:	Yogisha Mallya, Arun H. Shastry, M. S. Vidya
Rok vydání:	2020
Předmět:	medicine.medical_specialty medicine.diagnostic_test Computer science business.industry Event (computing) Deep learning Intracranial Hemorrhages Treatment process Computed tomography Workflow medicine Medical physics Artificial intelligence business Strengths and weaknesses
DOI:	10.1016/b978-0-12-820024-7.00004-9
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::422a2a5fc084e94eba24392001fb2430 https://doi.org/10.1016/b978-0-12-820024-7.00004-9 Zobrazit plný text záznamu