Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke

A. Hilbert, L. A. Ramos*, H. J. A. van Os, S. D. Olabarriaga, M. L. Tolhuisen, M. J. H. Wermer, R. S. Barros, I. van der Schaaf, D. Dippel, Y. B. W. E. M. Roos, W. H. van Zwam, A. J. Yoo, B. J. Emmer, G. J. Lycklama a Nijeholt, A. H. Zwinderman, G. J. Strijkers, C. B. L. M. Majoie, H. A. Marquering

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Article number103516
Number of pages7
JournalComputers in Biology and Medicine
Volume115
DOIs
Publication statusPublished - Dec 2019

Keywords

  • Acute ischemic stroke
  • Radiological images
  • Deep learning
  • Prognostics
  • ResNet
  • RFNN
  • Structured receptive fields
  • Gradient-weighted class activation mapping
  • COMPUTED-TOMOGRAPHY
  • ANGIOGRAPHY
  • SCORE

Cite this

Hilbert, A., Ramos, L. A., van Os, H. J. A., Olabarriaga, S. D., Tolhuisen, M. L., Wermer, M. J. H., Barros, R. S., van der Schaaf, I., Dippel, D., Roos, Y. B. W. E. M., van Zwam, W. H., Yoo, A. J., Emmer, B. J., Nijeholt, G. J. L. A., Zwinderman, A. H., Strijkers, G. J., Majoie, C. B. L. M., & Marquering, H. A. (2019). Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Computers in Biology and Medicine, 115, [103516]. https://doi.org/10.1016/j.compbiomed.2019.103516