Polyp-DAM: Polyp segmentation via depth anything model
This is an incremental improvement for medical imaging, specifically in polyp segmentation tasks.
The paper tackles polyp segmentation by using the Depth Anything Model to provide depth priors, achieving promising performance and robustness to noisy images.
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}.