Diagnosis and Prognosis of Head and Neck Cancer Patients using
  Artificial Intelligence
Ikboljon Sobirov
Cancer is one of the most life-threatening diseases worldwide, and head and
neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases
recorded each year. Clinicians use medical imaging modalities such as computed
tomography and positron emission tomography to detect the presence of a tumor,
and they combine that information with clinical data for patient prognosis. The
process is mostly challenging and time-consuming. Machine learning and deep
learning can automate these tasks to help clinicians with highly promising
results. This work studies two approaches for H&N tumor segmentation: (i)
exploration and comparison of vision transformer (ViT)-based and convolutional
neural network-based models; and (ii) proposal of a novel 2D perspective to
working with 3D data. Furthermore, this work proposes two new architectures for
the prognosis task. An ensemble of several models predicts patient outcomes
(which won the HECKTOR 2021 challenge prognosis task), and a ViT-based
framework concurrently performs patient outcome prediction and tumor
segmentation, which outperforms the ensemble model.