Contextual Hourglass Networks for Segmentation and Density Estimation
This incremental improvement addresses segmentation and counting challenges in the medical domain, potentially enhancing diagnostic accuracy.
The paper tackled the problem of improving hourglass networks for medical image segmentation and object counting by enabling integration of feature maps across layers with different spatial dimensions, achieving competitive results with up to 17 percentage point improvements over popular networks.
Hourglass networks such as the U-Net and V-Net are popular neural architectures for medical image segmentation and counting problems. Typical instances of hourglass networks contain shortcut connections between mirroring layers. These shortcut connections improve the performance and it is hypothesized that this is due to mitigating effects on the vanishing gradient problem and the ability of the model to combine feature maps from earlier and later layers. We propose a method for not only combining feature maps of mirroring layers but also feature maps of layers with different spatial dimensions. For instance, the method enables the integration of the bottleneck feature map with those of the reconstruction layers. The proposed approach is applicable to any hourglass architecture. We evaluated the contextual hourglass networks on image segmentation and object counting problems in the medical domain. We achieve competitive results outperforming popular hourglass networks by up to 17 percentage points.