MS-PriGAN: prior knowledge-based multi-scale gradient generation adversarial network for ovarian cancer CT image generation Online publication date: Mon, 25-Jul-2022
by Xun Wang; Zhiyong Yu; Lisheng Wang; Mao Ding
International Journal of Adaptive and Innovative Systems (IJAIS), Vol. 3, No. 2, 2022
Abstract: Despite the great success of deep neural networks in medical lesion detection tasks, their adaptation to CT images of ovarian cancer is known to be difficult, and the most important reason is the lack of ovarian cancer datasets. A commonly accepted reason for this deficiency is that few people are willing to disclose CT imaging data due to privacy concerns, despite the fact that ovarian cancer is the first most prevalent cancer among women. In this work, we propose a method for generating synthetic high-resolution medical images using generative adversarial networks. The method addresses the instability of previous work in generating work on small-scale datasets by a simple and effective technique to acquire prior knowledge and achieve multi-scale gradient flow. We show that the proposed method still achieves good results even when synthesised on small-scale data, and with FID scores better than the current top-performing networks (GANs).
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