SinGAN-Seg: Synthetic training data generation for medical image segmentation

@article{Thambawita2022SinGANSegST,
  title={SinGAN-Seg: Synthetic training data generation for medical image segmentation},
  author={Vajira Lasantha Thambawita and Pegah Salehi and Sajad Amouei Sheshkal and S. Hicks and Hugo L.Hammer and Sravanthi Parasa and Thomas de Lange and Paal Halvorsen and M. Riegler},
  journal={PLoS ONE},
  year={2022},
  volume={17}
}
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due… 

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