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Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while minimizing doses to healthy tissue. In order to guarantee that the prescribed radiation dose is delivered to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging using methods which can image prompt gamma rays that are emitted along the beam’s path through the patient such as Compton cameras (CC). However, because of limitations of the CC, their images are noisy and unusable for verifying proton treatment delivery. We provide a detailed description of a deep residual fully connected neural network that is capable of classifying and improving measured CC data with an increase in the fraction of usable data by up to 72% and allows for improved image reconstruction across the full range of clinical treatment delivery conditions. 

Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic weather patterns much more rapidly, thus allowing for more timely alerts to the public. To deal with class imbalance challenges in machine learning, different data augmentation approaches have been proposed. In this work, we examine the wall time difference between live data augmentation methods versus the use of preaugmented data when they are used in a convolutional neural network based training for tornado prediction. We also compare CPU and GPU based training over varying sizes of augmented data sets. Additionally we examine what impact varying the number of GPUs used for training will produce given a convolutional neural network.