论文标题
RDCNET：使用简约的复发性残留网络进行实例分割
RDCNet: Instance segmentation with a minimalist recurrent residual network
论文作者
论文摘要
实例分割是定量显微镜的关键步骤。尽管已经针对此问题提出了几种基于机器学习的方法，但其中大多数依赖于经过替代任务培训的计算复杂模型。基于对端到端可训练实例细分的最新发展，我们提出了一个极简主义的经常性网络，称为复发性扩张卷积网络（RDCNET），该网络由共享堆叠的散布卷积（SSDC）层组成，它迭代地迭代地改进了其输出，从而产生了可解释的中间预测。 It is light-weight and has few critical hyperparameters, which can be related to physical aspects such as object size or density.We perform a sensitivity analysis of its main parameters and we demonstrate its versatility on 3 tasks with different imaging modalities: nuclear segmentation of H&E slides, of 3D anisotropic stacks from light-sheet fluorescence microscopy and leaf segmentation of top-view images of plants.它在3个数据集中的2个中实现了最新的。
Instance segmentation is a key step for quantitative microscopy. While several machine learning based methods have been proposed for this problem, most of them rely on computationally complex models that are trained on surrogate tasks. Building on recent developments towards end-to-end trainable instance segmentation, we propose a minimalist recurrent network called recurrent dilated convolutional network (RDCNet), consisting of a shared stacked dilated convolution (sSDC) layer that iteratively refines its output and thereby generates interpretable intermediate predictions. It is light-weight and has few critical hyperparameters, which can be related to physical aspects such as object size or density.We perform a sensitivity analysis of its main parameters and we demonstrate its versatility on 3 tasks with different imaging modalities: nuclear segmentation of H&E slides, of 3D anisotropic stacks from light-sheet fluorescence microscopy and leaf segmentation of top-view images of plants. It achieves state-of-the-art on 2 of the 3 datasets.
