Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge

Autor:	Vahe M. Zohrabian, Christie Lincoln, Adam E. Flanders, Amit Agarwal, George Shih, David Joyner, Fanny Morón, Safwan Halabi, Rsna-Asnr Brain Hemorrhage Ct Annotators, Jaya Nath, R. Lee, Matthew P. Lungren, John Mongan, Robyn L. Ball, Luciano M. Prevedello, Gagandeep Choudhary, Lesley Cala, Felipe Kitamura, Anouk Stein, Ichiro Ikuta, Elka Miller, Monique A. Mogensen, Olivia McDonnell, Luiz Coelho, Jayashree Kalpathy-Cramer, Lubdha Shah
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	medicine.medical_specialty BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences Radiological and Ultrasound Technology business.industry Vascular Malformations BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti MEDLINE Brain medicine.disease Data Resources nervous system diseases Brain ct Stroke Text mining Artificial Intelligence Medicine Intracranial Hemorrhages Radiology Nuclear Medicine and imaging Radiology cardiovascular diseases Erratum business Intraparenchymal hemorrhage
Zdroj:	Radiol Artif Intell Radiology: Artificial Intelligence Volume 2 Issue 3 Ichiro Ikuta, MD, MMSc
ISSN:	2638-6100
Popis:	This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e960be3da33b374e383540c571c85c9c https://europepmc.org/articles/PMC8082297/ Zobrazit plný text záznamu