S145 - A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction
William Le, Francisco Perdigón Romero, Samuel Kadoury
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Medical image classification performance worsens in multi-domain datasets, caused by radiological image differences across institutions, scanner manufacturer, model and operator. Deep learning is well-suited for learning image features with priors encoded as constraints during the training process. In this work, we apply a ResNeXt classification network augmented with an FCN preprocessor subnetwork 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 acts as a embedding normalizer and improves over state-of-the-art results of 70% AUC to 76%.
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Poster Session #4 - 14:30 - 16:00 UTC-4 (Tuesday)
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