Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation

Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to segmentation of white matter lesions

Investigating the feature collection for semantic segmentation via single skip connection

Since the study of deep convolutional neural network became prevalent, one of the important discoveries is that a feature map from a convolutional network can be extracted before going into the fully connected layer and can be used as a saliency map for object detection. Furthermore, the model can use features from each different layer for accurate object detection: the features from different ...

Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer ...

Deeply supervised neural network with short connections for retinal vessel segmentation

The condition of vessel of the human eye is a fundamental factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus image is a challenging task due to low contrast, the presence of microaneurysms and hemorrhages. In this paper, we present a multiscale and multi-level deeply supervised convolutional neural network with short connections for vessel segmentation. We use ...

The Importance of Skip Connections in Biomedical Image Segmentation

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are sim...