M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images

TL;DR

This paper introduces M3FPolypSegNet, a novel multi-frequency feature fusion neural network that improves polyp segmentation accuracy in colonoscopy images by decomposing images into frequency components and applying multi-task learning.

Contribution

The paper presents a new frequency-based fully convolutional network with multi-frequency encoders and a scalable attention module for enhanced polyp segmentation performance.

Findings

Outperformed existing models with 6.92% and 7.52% performance gains on two datasets.

Effectively decomposed images into frequency components for better feature extraction.

Achieved superior segmentation accuracy across multiple metrics.

Abstract

Polyp segmentation is crucial for preventing colorectal cancer a common type of cancer. Deep learning has been used to segment polyps automatically, which reduces the risk of misdiagnosis. Localizing small polyps in colonoscopy images is challenging because of its complex characteristics, such as color, occlusion, and various shapes of polyps. To address this challenge, a novel frequency-based fully convolutional neural network, Multi-Frequency Feature Fusion Polyp Segmentation Network (M3FPolypSegNet) was proposed to decompose the input image into low/high/full-frequency components to use the characteristics of each component. We used three independent multi-frequency encoders to map multiple input images into a high-dimensional feature space. In the Frequency-ASPP Scalable Attention Module (F-ASPP SAM), ASPP was applied between each frequency component to preserve scale information. Subsequently, scalable attention was applied to emphasize polyp regions in a high-dimensional feature space. Finally, we designed three multi-task learning (i.e., region, edge, and distance) in four decoder blocks to learn the structural characteristics of the region. The proposed model outperformed various segmentation models with performance gains of 6.92% and 7.52% on average for all metrics on CVC-ClinicDB and BKAI-IGH-NeoPolyp, respectively.