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:
this https URL

本研究提出了使用 Meta-Former 与 UNet 的融合模型，并引入多尺度上采样块与级联组合以提高纹理，并提出 Convformer 块增强局部特征，以实现医学分割的全局信息、局部信息和边界信息等敏感点的更好决策。该模型在 CVC-300 数据集、Kvasir、CVC-ColonDB 数据集上取得了领先水平的表现。

元 - 息肉：高效息肉分割的基线模型

Meta-Polyp: a baseline for efficient Polyp segmentation

Recently, vision transformers started to show impressive results which
outperform large convolution based models significantly. However, in the area
of small models for mobile or resource constrained devices, ConvNet still has
its own advantages in both performance and model complexity. We propose
ParC-Net, a pure ConvNet based backbone model that further strengthens these
advantages by fusing the merits of vision transformers into ConvNets.
Specifically, we propose position aware circular convolution (ParC), a
light-weight convolution op which boasts a global receptive field while
producing location sensitive features as in local convolutions. We combine the
ParCs and squeeze-exictation ops to form a meta-former like model block, which
further has the attention mechanism like transformers. The aforementioned block
can be used in plug-and-play manner to replace relevant blocks in ConvNets or
transformers. Experiment results show that the proposed ParC-Net achieves
better performance than popular light-weight ConvNets and vision transformer
based models in common vision tasks and datasets, while having fewer parameters
and faster inference speed. For classification on ImageNet-1k, ParC-Net
achieves 78.6% top-1 accuracy with about 5.0 million parameters, saving 11%
parameters and 13% computational cost but gaining 0.2% higher accuracy and 23%
faster inference speed (on ARM based Rockchip RK3288) compared with MobileViT,
and uses only 0.5 times parameters but gaining 2.7% accuracy compared with
DeIT. On MS-COCO object detection and PASCAL VOC segmentation tasks, ParC-Net
also shows better performance. Source code is available at
this https URL

本文提出了一个基于轻量级 ConvNet 模型的元模型模块，结合视觉转换器（Vision Transformers）的优点，使用位置感知循环卷积（ParC）和压缩激活操作（squeeze-exictation ops）来加强 ConvNet 的性能，实现了比流行的轻量级 ConvNet 和基于视觉变换器的模型在常见视觉任务和数据集上更好的性能，而且具有更少的参数和更快的推理速度。