We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical imaging analysis. Existing approaches either applied multi-view planar (2D) networks or directly used volumetric (3D) networks for this purpose, but both of them are not ideal: 2D networks cannot capture 3D contexts effectively, and 3D networks are both memory-consuming and less stable arguably due to the lack of pre-trained models.In this paper, we bridge the gap between 2D and 3D using a novel approach named Elastic Boundary Projection (EBP). The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface. Therefore, we place a number of pivot points in the 3D space, and for each pivot, we determine its distance to the object boundary along a dense set of directions. This creates an elastic shell around each pivot which is initialized as a perfect sphere. We train a 2D deep network to determine whether each ending point falls within the object, and gradually adjust the shell so that it gradually converges to the actual shape of the boundary and thus achieves the goal of segmentation. EBP allows 3D segmentation without cutting the volume into slices or small patches, which stands out from conventional 2D and 3D approaches. EBP achieves promising accuracy in segmenting several abdominal organs from CT scans.
Many problems in RL, such as meta RL, robust RL, and generalization in RL, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques in their respective domains. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs. 1
The ability to collaborate with previously unseen human teammates is crucial for artificial agents to be effective in human-agent teams (HATs). Due to individual differences and complex team dynamics, it is hard to develop a single agent policy to match all potential teammates. In this paper, we study both human-human and humanagent teams in a dyadic cooperative task, Team Space Fortress (TSF). Results show that the team performance is influenced by both players' individual skill level and their ability to collaborate with different teammates by adopting complementary policies. Based on human-human team results, we propose an adaptive agent that identifies different human policies and assigns a complementary partner policy to optimize team performance. The adaptation method relies on a novel similarity metric to infer human policy and then selects the most complementary policy from a pre-trained library of exemplar policies. We conducted human-agent experiments to evaluate the adaptive agent and examine mutual adaptation in humanagent teams. Results show that both human adaptation and agent adaptation contribute to team performance.
This work studied human teamwork with a concentration on the influence of team synchronization and in- dividual differences on performance. Human participants were paired to complete collaborative tasks in a simulated game environment, in which they were assigned roles with corresponding responsibilities. Cross- correlation analysis was employed to quantify the degree of team synchronization and time-lag between two teammates’ collective actions. Results showed that team performance is determined by factors at both the individual and team levels. We found interaction effects between team synchronization and individual differences and quantified their contributions to team performance. The application of our research findings and proposed quantitative methods for developing adaptive agents for human-autonomy teaming is discussed.
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