Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation.

Few-shot medical image segmentation has achieved great progress in improving accuracy and efficiency of medical analysis in the biomedical imaging field. However, most existing methods cannot explore inter-class relations among base and novel medical classes to reason unseen novel classes. Moreover, the same kind of medical class has large intra-class variations brought by diverse appearances, shapes and scales, thus causing ambiguous visual characterization to degrade generalization performance of these existing methods on unseen novel classes. To address the above challenges, in this paper, we propose a Prototype correlation Matching and Class-relation Reasoning (i.e., PMCR) model. The proposed model can effectively mitigate false pixel correlation matches caused by large intra-class variations while reasoning inter-class relations among different medical classes. Specifically, in order to address false pixel correlation match brought by large intra-class variations, we propose a prototype correlation matching module to mine representative prototypes that can characterize diverse visual information of different appearances well. We aim to explore prototypelevel rather than pixel-level correlation matching between support and query features via optimal transport algorithm to tackle false matches caused by intra-class variations. Meanwhile, in order to explore inter-class relations, we design a class-relation reasoning module to segment unseen novel medical objects via reasoning inter-class relations between base and novel classes. Such inter-class relations can be well propagated to semantic encoding of local query features to improve few-shot segmentation performance. Quantitative comparisons illustrates the large performance improvement of our model over other baseline methods.

Characteristic time in highly motivated movements of children and adults through bottlenecks.

Current codes for fire protection of buildings are mainly based on the movement of adults and neglect the movement characteristic of pre-school children. Having a profound comprehension of the difference between children and adults passing bottlenecks is of great help to improve the safety levels of preschool children. This paper presents an experimental study on the bottleneck flow of pre-school children in a room. The movement characteristics of children's and adults' bottleneck flow are investigated with two macroscopic properties: density and speed profiles as well as microscopic characteristic time: motion activation time, relaxation time, exit travel time and time gap. Arch-like density distributions are observed both for highly motivated children and adults, while the distance between the peak density region and the exit location is shorter for children and longer for adults. Children's movement is less flexible manifested as longer motion activation time and longer relaxation time compared to that of adults. The findings from this study could enhance the understanding of crowd dynamics among the children population and provide supports for the scientific building design for children's facilities.