DeepAI AI Chat
Log In Sign Up

Generative Adversarial Network in Medical Imaging: A Review

09/19/2018
by   Xin Yi, et al.
0

Generative adversarial networks have gained a lot of attention in general computer vision community due to their capability of data generation without explicitly modelling the probability density function and robustness to overfitting. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into the training and imposing higher order consistency that is proven to be useful in many cases, such as in domain adaptation, data augmentation, and image-to-image translation. These nice properties have attracted researcher in the medical imaging community and we have seen quick adoptions in many traditional tasks and some novel applications. This trend will continue to grow based on our observation, therefore we conducted a review of the recent advances in medical imaging using the adversarial training scheme in the hope of benefiting researchers that are interested in this technique.

READ FULL TEXT
05/19/2020

Medical Image Generation using Generative Adversarial Networks

Generative adversarial networks (GANs) are unsupervised Deep Learning ap...
10/07/2020

Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications

While deep learning methods have shown great success in medical image an...
07/20/2021

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions

Despite technological and medical advances, the detection, interpretatio...
01/24/2022

Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation maps

Image translation across domains for unpaired datasets has gained intere...
04/22/2020

Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective

Exploiting learning algorithms under scarce data regimes is a limitation...
05/04/2018

Unsupervised learning for concept detection in medical images: a comparative analysis

As digital medical imaging becomes more prevalent and archives increase ...