Shallow Attention Network for Polyp Segmentation

By JunChao Wei, Yiwen Hu, Ruimao Zhang, Zhen Li, S.Kevin Zhou, Shuguang Cui. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021

417 citations recorded by the source corpus.

Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development of polyp segmentation. (i) Samples collected under different conditions show inconsistent colors, causing the feature distribution gap and overfitting issue; (ii) Due to repeated feature downsampling, small polyps are easily degraded; (iii) Foreground and background pixels are imbalanced, leading to a biased training. To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation. Specifically, to eliminate the effects of color, we design the color exchange operation to decouple the image contents and colors, and force the model to focus more on the target shape and structure. Furthermore, to enhance the segmentation quality of small polyps, we propose the shallow attention module to filter out the background noise of shallow features. Thanks to the high resolution of shallow features, small polyps can be preserved correctly. In addition, to ease the severe pixel imbalance for small polyps, we propose a probability correction strategy (PCS) during the inference phase. Note that even though PCS is not involved in the training phase, it can still work well on a biased model and consistently improve the segmentation performance. Quantitative and qualitative experimental results on five challenging benchmarks confirm that our proposed SANet outperforms previous state-of-the-art methods by a large margin and achieves a speed about 72FPS.

Subfields: Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/b3b3cb0dca8200dc64971020b39e036e64d72416

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ling Shao
2. Deng-Ping Fan
3. Huan Fu
4. Wenhai Wang
5. Ming-Ming Cheng
6. Jianbing Shen
7. Jinpeng Li
8. B. Dong

In Oncology

1. Deng-Ping Fan
2. Huan Fu

Citations in the corpus

• Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, methodology): “We collect several open source models from the field of polyp segmentation, for a total of nine comparative models, including U-Net [4], UNet++ [23], PraNet [5], SFA [90], MSEG [36], ACSNet [44], DCRNet [39], EUNet [47] and SANet [7].”