Paper ID: 2408.07262

Ensemble architecture in polyp segmentation

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

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 this https URL

Submitted: Aug 14, 2024