Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation
Lei Zhou
Abstract
Automated polyp segmentation technology plays an important role in diagnosing intestinal diseases, such as tumors and precancerous lesions. Previous works have typically trained convolution-based U-Net or Transformer-based neural network architectures with labeled data. However, the available public polyp segmentation datasets are too small to train the network sufficiently, suppressing each network's potential performance. To alleviate this issue, we propose a universal data augmentation technology, called SEP, to synthesize more data from the existing datasets. Specifically, we paste the polyp area with surroundings into the same image's background in a spatial-exclusive manner to obtain a combinatorial number of new images. SEP is a preprocessing method that can be incorporated into any existing network. For a fair comparison, we design a modular platform to facilitate conducting controlled experiments on various networks and datasets. Extensive results show that the proposed method enhances the data efficiency and achieves consistent improvements over baselines. Finally, we hit a new state of the art in this task.