Ensemble architecture in polyp segmentation
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910666297704448 |
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| author | Hsu, Hao-Yun Cheng, Yi-Ching Huang, Guan-Hua |
| author_facet | Hsu, Hao-Yun Cheng, Yi-Ching Huang, Guan-Hua |
| contents | This study explored the architecture of semantic segmentation and evaluated models that excel in polyp segmentation. We present an integrated framework that harnesses the advantages of different models to attain an optimal outcome. Specifically, in this framework, we fuse the learned features from convolutional and transformer models for prediction, thus engendering an ensemble technique to enhance model performance. Our experiments on polyp segmentation revealed that the proposed architecture surpassed other top models, exhibiting improved learning capacity and resilience. The code is available at https://github.com/HuangDLab/EnFormer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07262 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensemble architecture in polyp segmentation Hsu, Hao-Yun Cheng, Yi-Ching Huang, Guan-Hua Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This study explored the architecture of semantic segmentation and evaluated models that excel in polyp segmentation. We present an integrated framework that harnesses the advantages of different models to attain an optimal outcome. Specifically, in this framework, we fuse the learned features from convolutional and transformer models for prediction, thus engendering an ensemble technique to enhance model performance. Our experiments on polyp segmentation revealed that the proposed architecture surpassed other top models, exhibiting improved learning capacity and resilience. The code is available at https://github.com/HuangDLab/EnFormer. |
| title | Ensemble architecture in polyp segmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.07262 |