@misc{15456,
  keywords = {deep learning, Polyp segmentation, Semantic segmentation, colonoscopy, health informatics, Medical image segmentation},
  author = {Debesh Jha and Pia Smedsrud and Michael Riegler and Dag Johansen and Thomas de Lange and P{\r a}l Halvorsen and H{\r a}vard Johansen},
  title = {ResUNet++: An Advanced Architecture for Medical Image Segmentation},
  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.},
  year = {2019},
  journal = {2019 IEEE International Symposium on Multimedia (ISM)},
  publisher = {IEEE},
  address = {San Diego, California, USA},
}