Title: Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence Show Chinese title

Title: 使用人工智能对头颈部癌症患者的诊断和预后

Abstract: 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. Show Chinese abstract

Abstract: 癌症是全球范围内最致命的疾病之一，头颈（H&N）癌是一种常见类型，每年有数十万新病例被记录在案。临床医生使用计算机断层扫描和正电子发射断层扫描等医学成像技术来检测肿瘤的存在，并结合临床数据进行患者预后分析。这一过程极具挑战性且耗时较长。机器学习和深度学习可以自动化这些任务，为临床医生提供高度可靠的结果。本研究探讨了两种用于头颈肿瘤分割的方法：(i) 探索和比较基于视觉变换器（ViT）和卷积神经网络（CNN）的模型；以及(ii) 提出一种新颖的处理三维数据的二维视角方法。此外，本研究还提出了两种新的用于预后任务的架构。多个模型的集成预测患者预后结果（赢得了HECKTOR 2021挑战赛的预后任务），而基于ViT的框架同时执行患者预后预测和肿瘤分割，其表现优于集成模型。