Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a `wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ($54^3$) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.

本文提出了新型神经网络DAWMR，通过其宽结构，大视野，递归迭代和可并行架构等性质，实现了对图像标记问题的精确分类，并且在边界预测这一具体问题上，本文还介绍了一种改进的例子加权算法， DAQMR网络在3D电子显微镜数据的神经回路重建方面也表现出良好的性能。

用于强健图像标注的深度和广度多尺度递归网络