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In medical imaging, radiological scans of different modalities serve toenhance different sets of features for clinical diagnosis and treatmentplanning. This variety enriches the source information that could be used foroutcome prediction. Deep learning methods are particularly well-suited forfeature extraction from high-dimensional inputs such as images. In this work,we apply a CNN classification network augmented with a FCN preprocessorsub-network to a public TCIA head and neck cancer dataset. The training goal issurvival prediction of radiotherapy cases based on pre-treatment FDG PET-CTscans, acquired across 4 different hospitals. We show that the preprocessorsub-network in conjunction with aggregated residual connection leads toimprovements over state-of-the-art results when combining both CT and PET inputimages.

A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction