# SpectraNet: a novel model for polyp segmentation leveraging a spectral-guided mixture of functional experts

## TL;DR

SpectraNet is a new model that improves the accuracy of identifying polyps in colonoscopy images, helping detect colorectal cancer earlier.

## Contribution

The paper introduces SpectraNet, a novel hybrid-domain network with spectral-guided boundary enhancement and function-specialized experts for precise polyp segmentation.

## Key findings

- SpectraNet outperforms existing models on key segmentation metrics.
- The model produces more accurate segmentation masks with clearer boundaries.
- Experiments on multiple datasets confirm consistent performance improvements.

## Abstract

Automated and precise polyp segmentation from colonoscopy images is critical for the early diagnosis of colorectal cancer. However, this task is challenged by the ambiguous and low-contrast boundaries of polyps, which often blend with the surrounding mucosa. To address this, we propose SpectraNet, a novel hybrid-domain enhancement network for high-precision polyp segmentation. Our model is built on an encoder-decoder architecture with two core innovations integrated into its skip connections: (1) a Spectral-Guided Boundary Enhancement (SGBE) module that operates in the frequency domain to recover and sharpen indistinct boundary information by enhancing the phase spectrum of features, and (2) a Function-Specialized Mixture-of-Experts (FS-MoE) module that adaptively refines features for diverse polyp morphologies using a set of heterogeneous, function-specific experts. Extensive experiments on our curated PolypSegDataset and two public benchmarks (Kvasir-SEG and CVC-ClinicDB) demonstrate that our method consistently outperforms a wide range of state-of-the-art models. SpectraNet achieves superior performance in key segmentation metrics, and produces qualitatively more accurate segmentation masks with precise boundary definitions.