R2U
R2U, short for Recurrent Residual U-Net, is a deep learning architecture designed for medical image segmentation. It extends the U-Net framework by incorporating recurrent residual convolutional neural network blocks within the encoder and decoder paths. The recurrence enables the network to iteratively refine features, effectively increasing the receptive field without proportionally increasing the number of parameters.
In each stage, the standard convolutional block is replaced with an RRCNN block that applies a small
R2U has been applied to various medical segmentation tasks, such as cell nuclei, gland segmentation, and other
Impact and variants: R2U-inspired modules have influenced subsequent segmentation networks that seek to balance depth and
See also: U-Net; Residual networks; Recurrent convolutional networks; Medical image segmentation.