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Hamarneh Lab
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Hamarneh Lab
  • Home
  • Members
  • Research
  • Publications
  • Software
    • List of all software
    • SuperResNET
    • Skin3D
    • TurtleSeg
  • Data
    • List of all datasets
    • SkinIA
    • Ovarian Carcinomas Histopathology Dataset
  • Join our Team
  • Funding
  • Collaborators
  • Patents
  • Infrastructure
  • Teaching
  • Contact
  • News & Media
  • Cancer MIA
  • Internal
  • AgriTech
  • CS
  • SFU
  • More
    • Home
    • Members
    • Research
    • Publications
    • Software
      • List of all software
      • SuperResNET
      • Skin3D
      • TurtleSeg
    • Data
      • List of all datasets
      • SkinIA
      • Ovarian Carcinomas Histopathology Dataset
    • Join our Team
    • Funding
    • Collaborators
    • Patents
    • Infrastructure
    • Teaching
    • Contact
    • News & Media
    • Cancer MIA
    • Internal
    • AgriTech
    • CS
    • SFU

Cancer MIA

We focus on developing computer vision and machine and deep learning techniques to automatically interpret medical images for cancer applications, e.g.

  • analysis of super-resolution single molecule localization microscopy data to understand cellular architecture of of prostate cancer cells

  • histopathology based automated subtype classification of ovarian and breast cancer

  • quantitative imaging from PET/CT (relation to ct-DNA) for head and neck cancer

  • brachytherapy dose accumulation related to cervical cancer

  • skin dermoscopy image analysis for melanoma diagnosis

  • image guided robotic surgery and augmented reality for kidney cancer (partial nephrectomy)

  • enhancement and analysis of confocal laser endoscopy for gastrointestinal cancer diagnosis

  • deep learning based image reconstruction of diffuse optical tomography for breast cancer detection

  • brain tumour management from MRI

© 2019-2022  Ghassan Hamarneh
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