  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.
