CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation
This work addresses polyp segmentation for medical screening, but it is incremental as it builds on existing methods with specific architectural improvements.
The paper tackled automated colonic polyp segmentation by proposing CMFDNet, which addresses challenges like shape variation and indistinct boundaries, achieving mDice score improvements of 1.83% and 1.55% over SOTA methods on ETIS and ColonDB datasets.
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.