Keywords: Regularization, Generalization, Data Augmentation, Mixup, Image Segmentation
TL;DR: We suggest to apply manifold mixup on medical image segmentation.
Abstract: The scarcity of labeled data is a challenging problem in medical segmentation. Here, we suggest to apply manifold mixup, a recently proposed simple regularizer that utilizes linear combinations of hidden representations of training examples, on prostate cancer segmentation using MR image. Manifold mixup applied to either the encoder or decoder outperformed training without mixup and mixup applied on the input space.
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