Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.

Clicks: 361
ID: 61052
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #3 of 3 articles by views in medical image computing and computer-assisted intervention : miccai international conference on medical image computing and computer-assisted intervention

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network. Experiments on four brain MRI datasets indicate that our method yields registration performance that is promising in both accuracy and efficiency compared with state-of-the-art registration methods, including those based on deep learning.
Reference Key
fan2018adversarialmedical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fan, Jingfan;Cao, Xiaohuan;Xue, Zhong;Yap, Pew-Thian;Shen, Dinggang;
Journal medical image computing and computer-assisted intervention : miccai international conference on medical image computing and computer-assisted intervention
Year 2018
DOI
10.1007/978-3-030-00928-1_83
URL
Keywords
epicardial fat fat segmentation fruitfly algorithm gwo modified region growing optimal neural network

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.