# Ensemble architecture in polyp segmentation

Source: [Ensemble architecture in polyp segmentation](<https://www.sota2.com/research/paper/68fa75591e209131cea675d3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:32:00.805Z

Authors: Hao-Yun Hsu, Yi-Ching Cheng, Guan-Hua Huang

Published: 2024-08-14T02:57:38.000Z

## Abstract

This study explored the architecture of semantic segmentation and evaluated models that excel in polyp segmentation. We present an integrated framework that harnesses the advantages of different models to attain an optimal outcome. Specifically, in this framework, we fuse the learned features from convolutional and transformer models for prediction, thus engendering an ensemble technique to enhance model performance. Our experiments on polyp segmentation revealed that the proposed architecture surpassed other top models, exhibiting improved learning capacity and resilience. The code is available at https://github.com/HuangDLab/EnFormer.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.07262>)

[PDF](<https://arxiv.org/pdf/2408.07262.pdf>)