Strong-Guided Pixel Supervised Contrast for Polyp Segmentation
Keywords: polyp segmentation, contrast learning, strong- guided
Abstract: The current polyp segmentation methods mainly
use the saliency map to obtain the uncertain region, foreground
region, and background region of the polyp image, and then they
learn the semantic information from each other, to enhance the
edge segmentation ability of the network. However, there is great
instability in the quality of the saliency map and the error
information brought by low-quality saliency maps will interfere
with the segmentation ability of the network. To this end, this
paper proposes a strong-guided, pixel-wise, supervised
contrastive learning method (SGP-SCL), which enhance the
model to identify the polyp boundary by strengthening
foreground and background guidance for polyp boundary.
Specifically, the SGPS-CL method fully utilizes the ground truth
label to obtain high-confidence and representative samples to
guide the learning of boundary regions with low confidence, thus
reducing the impact of the instability of the preliminary
prediction probability map quality on the network performance.
Experiments are conducted on CVC-300, CVCClinicDB, Kvasir,
CVC-ColonDB, and ETIS polyp segmentation datasets, and the
proposed method achieves competitive results.
Submission Number: 6
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