Now showing items 1-5 of 5

    • Deep learning for accelerated 3D MRI 

      Özbey, Muzaffer (Bilkent University, 2021-08)
      Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The ...
    • Deep learning for accelerated MR imaging 

      Dar, Salman Ul Hassan (Bilkent University, 2021-02)
      Magnetic resonance imaging is a non-invasive imaging modality that enables multi-contrast acquisition of an underlying anatomy, thereby supplementing mul-titude of information for diagnosis. However, prolonged scan duration ...
    • Deep learning for digital pathology 

      Sarı, Can Taylan (Bilkent University, 2020-11)
      Histopathological examination is today’s gold standard for cancer diagnosis and grading. However, this task is time consuming and prone to errors as it requires detailed visual inspection and interpretation of a ...
    • Key protected classification for collaborative learning 

      Sarıyıldız, Mert Bülent; Cinbiş, R. G.; Ayday, Erman (Elsevier, 2020)
      Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving ...
    • Style synthesizing conditional generative adversarial networks 

      Çetin, Yarkın Deniz (Bilkent University, 2020-01)
      Neural style transfer (NST) models aim to transfer a particular visual style to a image while preserving its content using neural networks. Style transfer models that can apply arbitrary styles without requiring style-specific ...