A self-inverse network shares several distinct advantages: only one network instead of two, better generalization and more restricted parameter space. Most ...

In this paper, we explore the possibility of only wield- ing one network for bi-directional image synthesis. In other words, such an autonomous learning network ...

Sep 9, 2019 · In this paper, we explore the possibility of only wielding one network for bi-directional image synthesis. In other words, such an autonomous ...

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision ...

In this paper, our goal is to demonstrate that, for MRI image synthesis and other tasks, we are able to learn the above two tasks simultaneously using only one ...

In this paper, we explore the possibility of only wielding one network for bi-directional image synthesis. In other words, such an autonomous learning network ...

Learning to map between domains. Z Shen. University of Illinois at Urbana-Champaign, 2021. 2021. Learning A Self-Inverse Network for Bidirectional Mri Image ...

Here we propose a self-inverse network learning approach for unpaired image-to-image translation. Building on top of CycleGAN, we learn a self-inverse function ...

Here we propose a self-inverse network learning approach for unpaired image-to-image translation. Building on top of CycleGAN, we learn a self- inverse function ...

We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis.