Polyp-DAM: Polyp segmentation via depth anything model
Zhuoran Zheng Chen Wu Wei Wang Yeying Jin Xiuyi Jia
Recently, large models (Segment Anything model) came on the scene to provide
a new baseline for polyp segmentation tasks. This demonstrates that large
models with a sufficient image level prior can achieve promising performance on
a given task. In this paper, we unfold a new perspective on polyp segmentation
modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to
polyp segmentation models. Specifically, the input polyp image is first passed
through a frozen DAM to generate a depth map. The depth map and the input polyp
images are then concatenated and fed into a convolutional neural network with
multiscale to generate segmented images. Extensive experimental results
demonstrate the effectiveness of our method, and in addition, we observe that
our method still performs well on images of polyps with noise. The URL of our
code is \url{https://github.com/zzr-idam/Polyp-DAM}.