Existing studies tend tofocus onmodel modifications and integration with higher accuracy, which improve performance but also carry huge computational costs, resulting in longer detection times. Inmedical imaging, the use of time is extremely sensitive. And at present most of the semantic segmentation models have encoder-decoder structure or double branch structure. Their several times of the pooling use with high-level semantic information extraction operation cause information loss although there si a reverse pooling or other similar action to restore information loss of pooling operation. In addition, we notice that visual attention mechanism has superior performance on a variety of tasks. Given this, this paper proposes non-pooling network(NPNet), non-pooling commendably reduces the loss of information and attention enhancement m o d u l e ( A M ) effectively increases the weight of useful information. The method greatly reduces the number of parametersand computation costs by the shallow neural network structure. We evaluate the semantic segmentation model of our NPNet on three benchmark datasets comparing w i t h multiple current state-of-the-art(SOTA) models, and the implementation results show thatour NPNetachieves SOTA performance, with an excellent balance between accuracyand speed.

翻译：现有研究倾向于关注模型修改与集成以提高准确率，这虽提升了性能，但也带来了巨大的计算成本，导致检测时间延长。在医学图像领域，时间使用极为敏感。目前大多数语义分割模型采用编码器-解码器结构或双分支结构。这些模型多次使用池化操作进行高层语义信息提取，尽管存在逆向池化或其他类似操作来恢复信息损失，但池化仍会导致信息丢失。此外，我们注意到视觉注意力机制在多种任务中展现出优越性能。基于此，本文提出无池化网络（NPNet），其无池化设计显著减少了信息损失，而注意力增强模块（AM）则有效提高了有用信息的权重。该方法通过浅层神经网络结构大幅减少了参数量和计算成本。我们在三个基准数据集上评估了NPNet的语义分割模型，并与多个当前最优（SOTA）模型进行了比较。实验结果表明，NPNet实现了SOTA性能，并在精度与速度之间取得了优异平衡。