Construction of a machine learning dataset through collaboration: The RSNA 2019 brain CT hemorrhage challenge

RSNA-ASNR 2019 Brain Hemorrhage CT Annotators

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere190211
JournalRadiology: Artificial Intelligence
Volume2
Issue number3
DOIs
StatePublished - May 2020
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence
  • Radiological and Ultrasound Technology

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