Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
- URL: http://arxiv.org/abs/2212.01772v1
- Date: Sun, 4 Dec 2022 09:01:33 GMT
- Title: Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
- Authors: Usama Tariq, Rizwan Qureshi, Anas Zafar, Danyal Aftab, Jia Wu, Tanvir
Alam, Zubair Shah, Hazrat Ali
- Abstract summary: We present a method to generate brain tumor MRI images using generative adversarial networks.
Results demonstrate that the proposed method can learn the distributions of brain tumors.
The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors.
- Score: 6.244557340851846
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative models have been very successful over the years and have received
significant attention for synthetic data generation. As deep learning models
are getting more and more complex, they require large amounts of data to
perform accurately. In medical image analysis, such generative models play a
crucial role as the available data is limited due to challenges related to data
privacy, lack of data diversity, or uneven data distributions. In this paper,
we present a method to generate brain tumor MRI images using generative
adversarial networks. We have utilized StyleGAN2 with ADA methodology to
generate high-quality brain MRI with tumors while using a significantly smaller
amount of training data when compared to the existing approaches. We use three
pre-trained models for transfer learning. Results demonstrate that the proposed
method can learn the distributions of brain tumors. Furthermore, the model can
generate high-quality synthetic brain MRI with a tumor that can limit the small
sample size issues. The approach can addresses the limited data availability by
generating realistic-looking brain MRI with tumors. The code is available at:
~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.
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