A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…Particularly, we present a novel non-sequential multi-pretraining U-Net architecture and demonstrate that employing a number of persistent parallel models can boost the effectiveness of the segmentation procedures. The proposed approach makes advantage of data augmentation to generate newly synthesized images, which are subsequently processed using a watershed mask. …”