Ensemble architecture in polyp segmentation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 48 references that are in the map. First posted on arXiv 14 Aug 2024.
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36 of 48 references.
References · 36
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24 more
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