论文标题
通过实例学习弱监督的核分割
Weakly Supervised Nuclei Segmentation via Instance Learning
论文作者
论文摘要
弱监督的核分割是病理图像分析的关键问题，并且由于标签成本的显着降低，因此很大程度上有益于社区。采用点注释，以前的方法主要依赖于核实例的表达式较低，因此难以处理拥挤的核。在本文中，我们建议将弱监督的语义和实例细分分割，以使更有效的子任务学习并促进实例意识到的表示学习。为了实现这一目标，我们设计了一个具有两个分支的模块化深网络：语义提案网络和一个实例编码网络，该网络以两阶段的方式进行了培训，并具有实例敏感的损失。经验结果表明，我们的方法在不同类型的器官的病理图像的两个公共基准上实现了最新的表现。
Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost. Adopting point annotations, previous methods mostly rely on less expressive representations for nuclei instances and thus have difficulty in handling crowded nuclei. In this paper, we propose to decouple weakly supervised semantic and instance segmentation in order to enable more effective subtask learning and to promote instance-aware representation learning. To achieve this, we design a modular deep network with two branches: a semantic proposal network and an instance encoding network, which are trained in a two-stage manner with an instance-sensitive loss. Empirical results show that our approach achieves the state-of-the-art performance on two public benchmarks of pathological images from different types of organs.
