MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation

TL;DR

MicroAUNet is a lightweight, attention-based neural network designed for real-time colonoscopy polyp segmentation, combining multi-scale boundary features with knowledge distillation to achieve high accuracy with low computational cost.

Contribution

It introduces a novel, efficient architecture with boundary-enhanced multi-scale fusion and a two-stage knowledge distillation scheme for improved polyp segmentation.

Findings

Achieves state-of-the-art accuracy with low model complexity

Suitable for real-time clinical applications

Outperforms existing models on benchmark datasets

Abstract

Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry. However, current deep learning-based polyp segmentation models either compromise clinical decision-making by providing ambiguous polyp margins in segmentation outputs or rely on heavy architectures with high computational complexity, resulting in insufficient inference speeds for real-time colorectal endoscopic applications. To address this problem, we propose MicroAUNet, a light-weighted attention-based segmentation network that combines depthwise-separable dilated convolutions with a single-path, parameter-shared channel-spatial attention block to strengthen multi-scale boundary features. On the basis of it, a progressive two-stage knowledge-distillation scheme is introduced to transfer semantic and boundary cues from a high-capacity teacher. Extensive experiments on benchmarks also demonstrate the state-of-the-art accuracy under extremely low model complexity, indicating that MicroAUNet is suitable for real-time clinical polyp segmentation. The code is publicly available at https://github.com/JeremyXSC/MicroAUNet.