  In recent years, polyp segmentation has gained significant importance, and
many methods have been developed using CNN, Vision Transformer, and Transformer
techniques to achieve competitive results. However, these methods often face
difficulties when dealing with out-of-distribution datasets, missing
boundaries, and small polyps. In 2022, Meta-Former was introduced as a new
baseline for vision, which not only improved the performance of multi-task
computer vision but also addressed the limitations of the Vision Transformer
and CNN family backbones. To further enhance segmentation, we propose a fusion
of Meta-Former with UNet, along with the introduction of a Multi-scale
Upsampling block with a level-up combination in the decoder stage to enhance
the texture, also we propose the Convformer block base on the idea of the
Meta-former to enhance the crucial information of the local feature. These
blocks enable the combination of global information, such as the overall shape
of the polyp, with local information and boundary information, which is crucial
for the decision of the medical segmentation. Our proposed approach achieved
competitive performance and obtained the top result in the State of the Art on
the CVC-300 dataset, Kvasir, and CVC-ColonDB dataset. Apart from Kvasir-SEG,
others are out-of-distribution datasets. The implementation can be found at:
https://github.com/huyquoctrinh/MetaPolyp-CBMS2023.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning