CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation
<|reference_start|>CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation: 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.<|reference_end|>
@article{arudkar2024cris:,
    title={CRIS: Collaborative Refinement Integrated with Segmentation for Polyp
  Segmentation},
    author={Ankush Gajanan Arudkar, Bernard J.E. Evans},
    journal={arXiv preprint arXiv:2405.19672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19672},
    primaryClass={eess.IV cs.CV cs.LG}
}