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

Adam E Flanders Luciano M Prevedello George Shih Safwan S Halabi Jayashree Kalpathy-Cramer Robyn L Ball John T Mongan Anouk Stein Felipe Campos Kitamura Matthew P LungrenGagandeep ChoudharyLesley CalaLuiz Otavio de Mattos Coelho Monique A MogensenFanny MorónElka MillerIchiro Ikuta Vahe ZohrabianOlivia McDonnellChristie M Lincoln Lubdha M Shah David Joyner Amit Kumar Agarwal Ryan K LeeJaya Nathnull null

Published in: Radiology. Artificial intelligence (2020)

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT.

Keyphrases

machine learning
image quality
resting state
dual energy
computed tomography
white matter
contrast enhanced
functional connectivity
spinal cord
positron emission tomography
cerebral ischemia
artificial intelligence
magnetic resonance
big data