Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation

In proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer, 2020
 

Abstract

CNNs represent the current state of the art for image classification, as well as for image segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards texture, and that reducing it can increase the network’s accuracy. We hypothesize that CNNs for medical image segmentation might suffer from a similar bias. We propose to reduce it by augmenting the training data with feature preserving smoothing, which reduces noise and high-frequency textural features, while preserving semantically meaningful boundaries. Experiments on multiple medical image segmentation tasks confirm that, especially when limited training data is available or a domain shift is involved, feature preserving smoothing can indeed serve as a simple and effective augmentation technique.

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Bibtex

@INPROCEEDINGS{Sheikh:MICCAI20,
     author = {Sheikh, Rasha and Schultz, Thomas},
      title = {Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image
               Segmentation},
  booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
       year = {2020},
  publisher = {Springer},
       note = {Accepted for publication.},
   abstract = {CNNs represent the current state of the art for image classification, as well as for image
               segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards
               texture, and that reducing it can increase the network’s accuracy. We hypothesize that CNNs for
               medical image segmentation might suffer from a similar bias. We propose to reduce it by augmenting
               the training data with feature preserving smoothing, which reduces noise and high-frequency textural
               features, while preserving semantically meaningful boundaries. Experiments on multiple medical image
               segmentation tasks confirm that, especially when limited training data is available or a domain
               shift is involved, feature preserving smoothing can indeed serve as a simple and effective
               augmentation technique.}
}