Towards Robust Polyp Segmentation: Multi-Focus Attention Network with Fine-grained Polyp Cues

Document Type

Conference Proceeding

Publication Date

6-30-2025

Department

Department of Biomedical Engineering

Abstract

Colorectal cancer (CRC) is one of the prominent causes of cancer-related morbidity and mortality worldwide. More AI-assisted methods are conducted for early polyp detection and segmentation to improve the screening efficacy. However, previous solutions generally exhibit weak segmentation performance due to irregular structures of polyps, while the model robustness suffers from background noise of homogeneous neighbors. To this end, we propose a novel Multi-Focus Attention Network (MFANet) to encode multi-dimensional information (i.e., scale, contour, and shape) as fine-grained cues for polyp segmentation. Concretely, a Scale-Residual-Aware Attention (SRAA) is designed to apply the residual operation over each layer of the feature pyramid architecture, which could minimize the feature interference among different scales. To improve the model robustness, a Geometry-Structure-Aware Attention (GSAA) is formulated to integrate and refine multi-dimensional geometric features via a Channel-Wise Enhance Attention (CWEA), which condenses the spatial information and recalibrates the channel importance for adaptive feature recalibration. Experiments on six public datasets indicate the effectiveness of the proposed method. Notably, on the more challenging BKAI dataset, which is featured by tiny polyps with serious interference of homogeneous neighboring region, our MFANet can outperform the state-of-the-art (SOTA) methods. Additionally, it is experimentally verified that our approach consistently exhibits better segmentation performance with higher robustness against different attack strategies (i.e., FGSM, WaNet and PGD).

Publication Title

Icmr 2025 Proceedings of the 2025 International Conference on Multimedia Retrieval

ISBN

9798400718779

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