Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Journal: Computer methods and programs in biomedicine
Published Date: Mar 30, 2022

Abstract

BACKGROUND AND OBJECTIVE: Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mortality and morbidity. The fast and accurate detection of ICH is important for the patient to get an early and efficient treatment. To improve this diagnostic process, the application of Deep Learning (DL) models on head CT scans is an active area of research. Although promising results have been obtained, many of the proposed models require slice-level annotations by radiologists, which are costly and time-consuming.

Authors

  • Miguel López-Pérez
    Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain.
  • Arne Schmidt
    Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18010, Spain. Electronic address: arne@decsai.ugr.es.
  • Yunan Wu
    From the Division of Cardiology, Department of Medicine and Bluhm Cardiovascular Institute (R.M.W.), Division of Neurointerventional Radiology (D.R.C.), Division of Interventional Radiology (N.X.), and Division of Thoracic Imaging (B.D.A., G.A.M., H.S., R.A., N.P.), Department of Radiology, Northwestern Memorial Hospital, 676 N St Clair St, Chicago, IL 60611; and Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Evanston, Ill (J.S., S.D., S.C., A.D., S.B., Y.W., A.K.K.).
  • Rafael Molina
    Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain.
  • Aggelos K Katsaggelos
    Department of Electrical and Computer Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois.

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