Using GANs to augment medical imaging data to improve diagnostic classification accuracy
This project uses a generative adversarial network to generate synthetic mammography data. Deep learning has the potential to play a transformative role in medicine, however accessing the data necessary to develop robust and reliable models is often difficult. Recent work in GANs has shown that generating sythetic data can improve classification results (Antoniou et al., 2017). The goal of this project is to explore the possibility that generating synthetic mammography data can improve classification of cancerous or benign abnormalities. Preliminary results suggest that there is a slight increase in sensitivity, however significant improvements need to be made to the GAN before making any definitive conclusions.
Relevant publications: Antoniou A, Storkey A, Edwards H. (2017) https://arxiv.org/pdf/1711.04340.pdf. Zhu X, Liu Y, Qin Z, Li J (2017) https://arxiv.org/pdf/1711.00648.pdf. Mahmood F, Chen R, Durr N (2017) https://arxiv.org/pdf/1711.06606.pdf. Gulshan V, Peng L, Coram M et al. (2016) JAMA 316(22):2402-2410.