Automatic diagnosis and prognosis of head and neck cancer using deep learning

  • Research theme/s:

    Medical imaging analysis

  • Principal investigator (PI):

    Dr. Mohammad Yaqub

  • Researcher/s

    Nil

  • Funding:

    MBZUAI

  • Department:

    Computer vision

  • Co-PI

    Nil

  • Student/s

    Ikboljon Sobirov

  • Collaborators / partners:

    Nil

Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is one of the most common types of cancer. Oncologically, the diagnosis of H and N cancer is performed using imaging modalities like computed tomography (CT) and positron emission tomography (PET). Clinicians spend hours, if not days, to manually delineate the tumor region. Deep learning (DL) can help automate this task, allowing faster, more consistent and equally accurate diagnosis and prognosis. In this work, we study different approaches of DL for the diagnosis of H and N cancer using multimodal data of CT and PET. Additionally, we perform prognosis using the imaging data and clinical records, achieving clinically reasonable results on both tasks.