TY - JOUR
T1 - Construction of a machine learning dataset through collaboration
T2 - The RSNA 2019 brain CT hemorrhage challenge
AU - RSNA-ASNR 2019 Brain Hemorrhage CT Annotators
AU - Flanders, Adam E.
AU - Prevedello, Luciano M.
AU - Shih, George
AU - Halabi, Safwan S.
AU - Kalpathy-Cramer, Jayashree
AU - Ball, Robyn
AU - Mongan, John T.
AU - Stein, Anouk
AU - Kitamura, Felipe C.
AU - Lungren, Matthew P.
AU - Choudhary, Gagandeep
AU - Cala, Lesley
AU - Coelho, Luiz
AU - Mogensen, Monique
AU - Morón, Fanny
AU - Miller, Elka
AU - Ikuta, Ichiro
AU - Zohrabian, Vahe
AU - McDonnell, Olivia
AU - Lincoln, Christie
AU - Shah, Lubdha
AU - Joyner, David
AU - Agarwal, Amit
AU - Lee, Ryan K.
AU - Nath, Jaya
N1 - Publisher Copyright:
© RSNA, 2020.
PY - 2020/5
Y1 - 2020/5
N2 - This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT. Key Points This 874-035-image, multi-institutional, and multinational brain hemorrhage CT dataset is the largest public collection of its kind that includes expert annotations from a large cohort of volunteer neuroradiologists for classifying intracranial hemorrhages. This dataset was used for the Radiological Society of North America (RSNA) 2019 Machine Learning Challenge. The curation of this dataset was a collaboration between the RSNA and the American Society of Neuroradiology and is made freely available to the machine learning research community for noncommercial use to create high-quality machine learning algorithms to help diagnose intracranial hemorrhage.
AB - This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT. Key Points This 874-035-image, multi-institutional, and multinational brain hemorrhage CT dataset is the largest public collection of its kind that includes expert annotations from a large cohort of volunteer neuroradiologists for classifying intracranial hemorrhages. This dataset was used for the Radiological Society of North America (RSNA) 2019 Machine Learning Challenge. The curation of this dataset was a collaboration between the RSNA and the American Society of Neuroradiology and is made freely available to the machine learning research community for noncommercial use to create high-quality machine learning algorithms to help diagnose intracranial hemorrhage.
UR - http://www.scopus.com/inward/record.url?scp=85112725947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112725947&partnerID=8YFLogxK
U2 - 10.1148/ryai.2020190211
DO - 10.1148/ryai.2020190211
M3 - Article
C2 - 33937827
AN - SCOPUS:85112725947
SN - 2638-6100
VL - 2
JO - Radiology: Artificial Intelligence
JF - Radiology: Artificial Intelligence
IS - 3
M1 - e190211
ER -