  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.
