Abstract #4718

A Generative Adversarial Network with a Progressively Growing Training Strategy for MRI Dataset Augmentation

Vahid K Ghodrati^1,2, Haotian An³, Zihao Xiong³, Jiaxin Shao¹, Mark Bydder¹, and Peng Hu¹

¹Radiology, University of California Los Angeles, Los Angeles, CA, United States, ²Biomedical Physics Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA, United States, ³Department of Electronic Engineering, Tsinghua University, Beijing, China

For medical imaging applications, it is not straightforward to create a large database due to high costs associated with acquiring the data, patent privacy issues, and challenges in pooling data from multiple medical institutions. Generating high-resolution medical images from the latent noise vector could potentially mitigate training data size issues in applying DNN to medical imaging. This could facilitate objective comparisons between the different machine learning algorithms in medical imaging. In this study, progressive growing strategy is considered to train the GAN stably and generate super resolution brain datasets from noise vector.

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