Chest x-ray generation and data augmentation for cardiovascular abnormality classification

By Ali Madani, Mehdi Moradi, Alexandros Karargyris, Tanveer Fathima Syeda-Mahmood. 2018

168 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

1. Daniel Rueckert

In Radiology, Nuclear Medicine and Imaging

1. Daniel Rueckert
2. Alexander Hammers
3. Paul S. Babyn
4. Xin Yi
5. Roger N. Gunn
6. Ekta Walia
7. Mert Rory Sabuncu
8. Martin J. Willemink

Citations in the corpus

• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018, background): “However, the use of nonconditional GANs to augment training data directly as a preprocessing step with no additional data has only very recently been explored [5,15], with promising results in medical image classification tasks.”
• Generative Adversarial Network in Medical Imaging: A Review (2018, background): “X-Ray [125], [104], [29], [90], [91], [44], [170], [19]”
• A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022, background): “If there is only one class in the datasets, such as medical images with one abnormal class [70], one unconditional image”