Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation

TL;DR

This paper introduces a universal data augmentation method called Spatially Exclusive Pasting for polyp segmentation, which synthesizes new training images by spatially exclusive pasting of polyps, improving model performance.

Contribution

The paper proposes a novel data augmentation technique that enhances polyp segmentation by generating diverse training images through spatially exclusive pasting, leading to improved accuracy.

Findings

Achieves state-of-the-art performance on polyp segmentation datasets.

Enhances data efficiency and model generalization.

Consistent improvements across various networks and datasets.

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 to synthesize more data from the existing datasets. Specifically, we paste the polyp area into the same image's background in a spatial-exclusive manner to obtain a combinatorial number of new images. Extensive experiments on various networks and datasets 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. We will release the code soon.