|Machine learning and|
The U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany. The network is based on the fully convolutional network  and its architecture was modified and extended to work with fewer training images and to yield more precise segmentations.
The network consists of a contracting path and an expansive path, which gives it the u-shaped architecture. The contracting path is a typical convolutional network that consists of repeated application of convolutions, each followed by a rectified linear unit (ReLU) and a max pooling operation. During the contraction, the spatial information is reduced while feature information is increased. The expansive pathway combines the feature and spatial information through a sequence of up-convolutions and concatenations with high-resolution features from the contracting path.
- Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas (2015). "U-Net: Convolutional Networks for Biomedical Image Segmentation". arXiv:1505.04597 [cs.CV].
- Long, J.; Shelhamer, E.; Darrell, T. (2014). "Fully convolutional networks for semantic segmentation". arXiv:1411.4038 [cs.CV].
- "U-Net code".
-  Google Scholar citation data
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