Shuting Huang

M.D.

Guangdong University of Technology

M.D.

NLST (Learn more about this study)

NLST-892

Mar 22, 2022

Using generative adversarial network for data augmentation to improve classification of lung cancer

Pathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotype in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the count of patients data. Generative Adversarial Networks have been used to synthesize medical images with moderate to good authenticity. We are working on building generative models to generate synthetic lung cancer images using pathology images. and we propose to use these synthetic images to further improve classification of lung cancer.
In short, NLST pathology image datasets are needed to enhance our method performance. Related works would be written as papers to facilitate the development of this field. Thank you!