# CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation

Source: [CRIS: Collaborative Refinement Integrated with Segmentation for Polyp Segmentation](<https://www.sota2.com/research/paper/68fa73601e209131cea5c003>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:48:16.237Z

Authors: Ankush Gajanan Arudkar, Bernard J.E. Evans

Published: 2024-05-30T03:56:01.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2405.19672>)

[PDF](<https://arxiv.org/pdf/2405.19672.pdf>)