Stateless actor-critic for instance segmentation with high-level priors
This addresses the annotation bottleneck for domain experts in biomedical imaging by enabling rule-based guidance without requiring differentiability.
The paper tackles the challenge of instance segmentation in computer vision, particularly in biomedical applications where obtaining labeled data is difficult, by using stateless actor-critic reinforcement learning to incorporate non-differentiable high-level priors, achieving excellent performance without direct supervision.
Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we relax this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real datasets demonstrate that we can achieve excellent performance without any direct supervision based only on a rich set of priors.