Ensemble architecture in polyp segmentation
This work addresses polyp segmentation for medical imaging, which is incremental as it combines existing methods into an ensemble framework.
The study tackled polyp segmentation by developing an ensemble architecture that fuses features from convolutional and transformer models, achieving improved performance over other top models with enhanced learning capacity and resilience.
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.