Ensemble architecture in polyp segmentation

TL;DR

This paper introduces an ensemble framework combining convolutional and transformer models for polyp segmentation, achieving superior performance and robustness compared to existing models.

Contribution

It presents a novel ensemble architecture that fuses features from different model types to improve polyp segmentation accuracy.

Findings

Outperformed existing top models in polyp segmentation

Enhanced learning capacity and resilience of the model

Code available for reproducibility

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.