Abstract Cell segmentation is a critical step in histology images analysis. Recently, Convolutional Neural Network (CNN) has shown outstanding performance for various segmentation problems, however, the segmentation of histology images remains challenging due to the tight arrangement of cells and their weak boundaries. This paper proposes a novel architecture called Attention-based Mixture of Experts (Att-MoE) for nuclear and cytoplasmic segmentation in fluorescent histology images, which integrates multiple Expert networks using a single Gating network. Expert networks complement each other to accomplish sub-tasks under the direction of the Gating network, which enforces the adaptive use of multiple networks to complete the segmentation task. The Att-MoE also introduces attention gates and residual blocks in the Expert networks to improve segmentation accuracy. The attention gate is used to emphasize useful features and suppress irrelevant features for segmentation in a self-adaptive manner. On the other hand, residual blocks are employed to enhance gradient flow in training and improve the stability and segmentation accuracy of the network. Experiments on fluorescent histology images of mouse liver show that Att-MoE is superior to recent segmentation methods and has the potential for cancer diagnosis based on histology images.

中文翻译：

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Att-MoE：基于注意力的核和细胞质分割专家组合

摘要 细胞分割是组织学图像分析的关键步骤。最近，卷积神经网络（CNN）在各种分割问题上表现出出色的性能，然而，由于细胞排列紧密且边界薄弱，组织学图像的分割仍然具有挑战性。本文提出了一种新的架构，称为基于注意力的专家混合（Att-MoE），用于荧光组织学图像中的核和细胞质分割，它使用单个门控网络集成了多个专家网络。专家网络在 Gating 网络的指导下相互补充完成子任务，强制使用多个网络来完成分割任务。Att-MoE 还在专家网络中引入了注意门和残差块以提高分割精度。注意门用于以自适应方式强调有用的特征并抑制不相关的特征进行分割。另一方面，使用残差块来增强训练中的梯度流，提高网络的稳定性和分割精度。对小鼠肝脏荧光组织学图像的实验表明，Att-MoE 优于最近的分割方法，具有基于组织学图像进行癌症诊断的潜力。残差块用于增强训练中的梯度流，提高网络的稳定性和分割精度。小鼠肝脏荧光组织学图像的实验表明，Att-MoE 优于最近的分割方法，具有基于组织学图像进行癌症诊断的潜力。残差块用于增强训练中的梯度流，提高网络的稳定性和分割精度。对小鼠肝脏荧光组织学图像的实验表明，Att-MoE 优于最近的分割方法，具有基于组织学图像进行癌症诊断的潜力。