Self-Configuring Capsule Networks for Brain Image Segmentation
Avesta, A. E.; Hossain, S.; Aboian, M.; Krumholz, H.; Aneja, S.
Show abstract
When an auto-segmentation model needs to be applied to a new segmentation task, multiple decisions should be made about the pre-processing steps and training hyperparameters. These decisions are cumbersome and require a high level of expertise. To remedy this problem, I developed self-configuring CapsNets (scCapsNets) that can scan the training data as well as the computational resources that are available, and then self-configure most of their design options. In this study, we developed a self-configuring capsule network that can configure its design options with minimal user input. We showed that our self-configuring capsule netwrok can segment brain tumor components, namely edema and enhancing core of brain tumors, with high accuracy. Out model outperforms UNet-based models in the absence of data augmentation, is faster to train, and is computationally more efficient compared to UNet-based models.
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