Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929376097992704 |
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| author | Galdran, Adrian |
| author_facet | Galdran, Adrian |
| contents | This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03901 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks Galdran, Adrian Image and Video Processing Computer Vision and Pattern Recognition Machine Learning This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts. |
| title | Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.03901 |