Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

Journal: International journal of computer assisted radiology and surgery
PMID: 36635594

Abstract

PURPOSE: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods in treatment decision-making.

Authors

  • Mumu Aktar
    Computer Science and Software Engineering, Concordia University, 1455 boul. De Maisonneuve O., Montreal, QC, H3G 1M8, Canada. m_ktar@encs.concordia.ca.
  • Jonatan Reyes
    Computer Science and Software Engineering, Concordia University, 1455 boul. De Maisonneuve O., Montreal, QC, H3G 1M8, Canada.
  • Donatella Tampieri
    Department of Radiology, Queen's University, Kingston, ON, Canada.
  • Hassan Rivaz
  • Yiming Xiao
  • Marta Kersten-Oertel
    Computer Science and Software Engineering, Concordia University, 1455 boul. De Maisonneuve O., Montreal, QC, H3G 1M8, Canada.

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