Bayesian optimization-derived batch size and learning rate scheduling in deep neural network training for head and neck tumor segmentation

Medical imaging is a key tool used in healthcare to diagnose and prognose patients by aiding the detection of a variety of diseases and conditions. In practice, medical image screening must be performed by clinical practitioners who rely primarily on their expertise and experience for disease diagnosis. The ability of convolutional neural networks (CNNs) to extract hierarchical features and determine classifications directly from raw image data makes CNNs a potentially useful adjunct to the medical image analysis process. A common challenge in successfully implementing CNNs is optimizing hyperparameters for training. In this study, we propose a method which utilizes scheduled hyperparameters and Bayesian optimization to segment cancerous and noncancerous tissues from head and neck computed tomography (CT) and positron emission tomography (PET) scans. The results of this method are compared using CT imaging with and without PET imaging.