CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917679210692608 |
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| author | Arudkar, Ankush Gajanan Evans, Bernard J. E. |
| author_facet | Arudkar, Ankush Gajanan Evans, Bernard J. E. |
| contents | Accurate detection of colorectal cancer and early prevention heavily rely on precise polyp identification during gastrointestinal colonoscopy. Due to limited data, many current state-of-the-art deep learning methods for polyp segmentation often rely on post-processing of masks to reduce noise and enhance results. In this study, we propose an approach that integrates mask refinement and binary semantic segmentation, leveraging a novel collaborative training strategy that surpasses current widely-used refinement strategies. We demonstrate the superiority of our approach through comprehensive evaluation on established benchmark datasets and its successful application across various medical image segmentation architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation Arudkar, Ankush Gajanan Evans, Bernard J. E. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Accurate detection of colorectal cancer and early prevention heavily rely on precise polyp identification during gastrointestinal colonoscopy. Due to limited data, many current state-of-the-art deep learning methods for polyp segmentation often rely on post-processing of masks to reduce noise and enhance results. In this study, we propose an approach that integrates mask refinement and binary semantic segmentation, leveraging a novel collaborative training strategy that surpasses current widely-used refinement strategies. We demonstrate the superiority of our approach through comprehensive evaluation on established benchmark datasets and its successful application across various medical image segmentation architectures. |
| title | CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.19672 |