Polyp-DAM: Polyp segmentation via depth anything model
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913222973456384 |
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| author | Zheng, Zhuoran Wu, Chen Wang, Wei Jin, Yeying Jia, Xiuyi |
| author_facet | Zheng, Zhuoran Wu, Chen Wang, Wei Jin, Yeying Jia, Xiuyi |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyp-DAM: Polyp segmentation via depth anything model Zheng, Zhuoran Wu, Chen Wang, Wei Jin, Yeying Jia, Xiuyi Computer Vision and Pattern Recognition 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}. |
| title | Polyp-DAM: Polyp segmentation via depth anything model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.02298 |