@inproceedings{a00b2c27b0be43d682a5e1ad7ece99d3,
title = "Improved segmentation by adversarial U-Net",
abstract = "Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U-Net that consists of an encoder-decoder based architecture. Many variations exist to the U-Net architecture. In this work, we present a new training procedure that combines U-Net with an adversarial training we refer to as Adversarial U-Net. We show that Adversarial U-Net outperformes the conventional U-Net in three versatile domains that differ in the acquisition method as well as the physical characteristics and yields smooth and improved segmentation maps.",
keywords = "Computer Assisted Diagnosis, Convolutional Neural Network, Deep Learning, Image Segmentation, U-Net",
author = "David Sriker and Dana Cohen and Noa Cahan and Hayit Greenspan",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Computer-Aided Diagnosis ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "https://doi.org/10.1117/12.2582130",
language = "الإنجليزيّة",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Mazurowski, {Maciej A.} and Karen Drukker",
booktitle = "Medical Imaging 2021",
address = "الولايات المتّحدة",
}