Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence
This addresses the time-consuming and challenging process of cancer diagnosis and prognosis for clinicians, though it is incremental as it builds on existing AI methods in medical imaging.
This work tackles the problem of automating tumor segmentation and prognosis for head and neck cancer patients using AI, achieving state-of-the-art results by winning the HECKTOR 2021 challenge prognosis task and outperforming ensemble models with a ViT-based framework.
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.