@inproceedings {26812,
	title = {ResUNet++: An Advanced Architecture for Medical Image Segmentation},
	journal = {2019 IEEE International Symposium on Multimedia (ISM)},
	year = {2019},
	publisher = {IEEE},
	address = {San Diego, California, USA},
	abstract = {Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33\%, and a mean Intersection over Union (mIoU) of 79.27\% for the Kvasir-SEG dataset and a dice coefficient of 79.55\%, and a mIoU of 79.62\% with CVC-612 dataset.},
	keywords = {colonoscopy, deep learning, health informatics, Medical image segmentation, Polyp segmentation, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l and H{\r a}vard D {Johansen}}
}
