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R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation
The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder.
Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR.
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