Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree structure for feature propagation we propose Receptive Field Chain Network (RFC-Net) that learns high resolution global features on a compressed computational space. Our experiments demonstrates that RFC-Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation.

翻译：在医学诊断中,具有下游和上游传播流动的进化神经网络(CNN)结构在分解方面很受欢迎。然而,由于在多个阶段进行空间下游取样和上层取样,信息损失是不可阻挡的。相反,以高空间分辨率为密度的层连接在计算上是昂贵的。在这项工作中,我们设计了一个Loose Dense连接战略,将随后各层的神经元与减少的参数连接起来。此外,我们利用一个用于特征传播的移动树结构,我们提议采用感知式外地链网(RFC-Net),在压缩计算空间上学习高分辨率的全球特征。我们的实验表明,RFC-Net在Kvasir和CVC-ClinicDB的聚合分解基准上取得了最先进的表现。