  In this paper, we present Crossing Aggregation Network (CAggNet), a novel
densely connected semantic segmentation approach for medical image analysis.
The crossing aggregation network improves the idea from deep layer aggregation
and makes significant innovations in semantic and spatial information fusion.
In CAggNet, the simple skip connection structure of general U-Net is replaced
by aggregations of multi-level down-sampling and up-sampling layers, which is a
new form of nested skip connection. This aggregation architecture enables the
network to fuse both coarse and fine features interactively in semantic
segmentation. It also introduces weighted aggregation module to up-sample
multi-scale output at the end of the network. We have evaluated and compared
our CAggNet with several advanced U-Net based methods in two public medical
image datasets, including the 2018 Data Science Bowl nuclei detection dataset
and the 2015 MICCAI gland segmentation competition dataset. Experimental
results indicate that CAggNet improves medical object recognition and achieves
a more accurate and efficient segmentation compared to existing improved U-Net
and UNet++ structure.
