CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation

TL;DR

CMFDNet is a novel deep learning architecture designed to improve colonic polyp segmentation by addressing shape variability, boundary ambiguity, and small polyp detection, outperforming state-of-the-art methods on key datasets.

Contribution

Introduces CMFDNet with three modules (CMD, MSA, FD) that enhance boundary clarity, recognition of diverse polyp sizes, and small polyp detection in segmentation tasks.

Findings

Outperforms six SOTA methods on ETIS and ColonDB datasets.

Achieves 1.83% and 1.55% higher mDice scores on key datasets.

Effectively reduces boundary blurriness and improves small polyp detection.

Abstract

Automated colonic polyp segmentation is crucial for assisting doctors in screening of precancerous polyps and diagnosis of colorectal neoplasms. Although existing methods have achieved promising results, polyp segmentation remains hindered by the following limitations,including: (1) significant variation in polyp shapes and sizes, (2) indistinct boundaries between polyps and adjacent tissues, and (3) small-sized polyps are easily overlooked during the segmentation process. Driven by these practical difficulties, an innovative architecture, CMFDNet, is proposed with the CMD module, MSA module, and FD module. The CMD module, serving as an innovative decoder, introduces a cross-scanning method to reduce blurry boundaries. The MSA module adopts a multi-branch parallel structure to enhance the recognition ability for polyps with diverse geometries and scale distributions. The FD module establishes dependencies among all decoder features to alleviate the under-detection of polyps with small-scale features. Experimental results show that CMFDNet outperforms six SOTA methods used for comparison, especially on ETIS and ColonDB datasets, where mDice scores exceed the best SOTA method by 1.83% and 1.55%, respectively.