论文标题
标准化的完全卷积方法来预测头部和颈癌预测
A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction
论文作者
论文摘要
在医学成像中，不同方式的放射学扫描有助于增强临床诊断和治疗计划的不同特征集。这种品种丰富了可用于结果预测的源信息。深度学习方法特别适合从高维输入（例如图像）中提取特征。在这项工作中，我们将CNN分类网络应用于FCN预处理器子网络增强的CNN分类网络上，将其应用于公共TCIA头和颈部癌症数据集。培训目标是基于治疗前FDG PET-CT扫描的放射治疗病例的生存预测，该病例在4家不同的医院中获得。我们表明，在结合CT和PET输入图像时，预处理程序子网络与汇总的残留连接结合结合在一起，可以改善对最先进的结果。
In medical imaging, radiological scans of different modalities serve to enhance different sets of features for clinical diagnosis and treatment planning. This variety enriches the source information that could be used for outcome prediction. Deep learning methods are particularly well-suited for feature extraction from high-dimensional inputs such as images. In this work, we apply a CNN classification network augmented with a FCN preprocessor sub-network to a public TCIA head and neck cancer dataset. The training goal is survival prediction of radiotherapy cases based on pre-treatment FDG PET-CT scans, acquired across 4 different hospitals. We show that the preprocessor sub-network in conjunction with aggregated residual connection leads to improvements over state-of-the-art results when combining both CT and PET input images.
