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Sydney Digital Theses (Open Access): Recent submissions

    • Data-efficient Cross-domain Medical Image Segmentation 

      Yu, Qianbi
      Published 2023
      Deep learning-based segmentation methods have been widely employed for medical image analysis, especially for automatic disease diagnosis and prognosis. Nevertheless, existing deep-learning models benefit from large amounts ...
      Open Access
      Thesis
      View
    • Lived experience of recovering from mental distress: people from Chinese cultural backgrounds living in Australia 

      Liu, Chenchen
      Published 2023
      This study explores the lived experiences of mental distress among people from Chinese cultural backgrounds in the Australian social context, through an intersectionality lens based on social constructionism and critical ...
      Open Access
      Thesis
      View
    • A Study of Galactic and Extragalactic Evolution with the MUSE Integral-Field Spectrograph 

      Wang, Zixian
      Published 2023
      The Milky Way is by far the best-studied galaxy in the Universe, and it has long been regarded as a benchmark and ideal laboratory for understanding galaxy evolution. Recent studies compared the Milky Way with its analogues ...
      Open Access
      Thesis
      View
    • The Interplay between Computation and Communication 

      Salari, Ayoob
      Published 2023
      In this thesis, a comprehensive exploration into the integration of communication and learning within the massive Internet of Things (mIoT) is undertaken. Addressing one of the fundamental challenges of mIoT, where traditional ...
      Open Access
      Thesis
      View
    • Deep Learning-based Radiomics Framework for Multi-Modality PET-CT Images 

      Peng, Yige
      Published 2023
      Multimodal positron emission tomography - computed tomography (PET-CT) imaging is widely regarded as the imaging modality of choice for cancer management. This is because PET-CT combines the high sensitivity of PET in ...
      Open Access
      Thesis
      View
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