CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation

• URL: http://arxiv.org/abs/2405.19672v1
• Date: Thu, 30 May 2024 03:56:01 GMT
• Title: CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation
• Authors: Ankush Gajanan Arudkar, Bernard J. E. Evans,
• Abstract summary: We propose an approach that integrates mask refinement and binary semantic segmentation. We demonstrate the superiority of our approach through comprehensive evaluation on established benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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