Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.
翻译:近年来,基于复杂的神经网络结构的日益复杂的方法逐渐缓慢地推动完善的基准数据集的性能。在本文中,我们退一步来审查这种复杂程度的实际需要。我们介绍了一个完全进化的网络RC-Net,这是一个完全进化的网络,每个层的过滤器数量得到优化,以减少特征重叠和复杂性。我们还利用跳过连接将网络中集中作业的数量保持在最低水平,从而将空间信息损失保持在最低水平。在我们的实验中,使用了两个公开提供的视网膜船舶分割数据集。在我们的实验中,RC-Net具有相当的竞争性,优异的替代船舶分割方法,有两个甚至三个规模不易培训的参数。