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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: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/rizwanqureshi123\/Brain-Tumor-Synthetic-Data\">https:\/\/github.com\/rizwanqureshi123\/Brain-Tumor-Synthetic-Data<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_12","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"147-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Brain Tumor Synthetic Data Generation with\u00a0Adaptive StyleGANs"],"prefix":"10.1007","author":[{"given":"Usama","family":"Tariq","sequence":"first","affiliation":[]},{"given":"Rizwan","family":"Qureshi","sequence":"additional","affiliation":[]},{"given":"Anas","family":"Zafar","sequence":"additional","affiliation":[]},{"given":"Danyal","family":"Aftab","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Tanvir","family":"Alam","sequence":"additional","affiliation":[]},{"given":"Zubair","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Hazrat","family":"Ali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","volume":"6","author":"J Ker","year":"2017","unstructured":"Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. 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