A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction
This work addresses outcome prediction for head and neck cancer patients undergoing radiotherapy, but it is incremental as it builds on existing deep learning methods with specific architectural enhancements.
The paper tackled survival prediction for head and neck cancer patients using pre-treatment PET-CT scans by applying a CNN classification network with a FCN preprocessor sub-network, achieving improvements over state-of-the-art results when combining both CT and PET images.
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.