This project uses conditional GAN with auxiliary classifier[1] to generate synthetic pathology data. The dataset used in this project is called PatchCamelyon or PCAM[2]. PCam dataset, derived from CAMELYON16[3], contains over two hundred thousand 96x96 breast histopathology slide image patches. The patches contain both clinically significant metastatic cancer and healthy cells. More information about the project can be found in this article
[1] Odena, A., Olah, C., & Shlens, J. (2017, July). Conditional image synthesis with auxiliary classifier gans. In International conference on machine learning (pp. 2642-2651).
[2] Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M. (2018, September). Rotation equivariant CNNs for digital pathology. In International Conference on Medical image computing and computer-assisted intervention (pp. 210-218). Springer, Cham.
[3] Bejnordi, B. E., Veta, M., Van Diest, P. J., Van Ginneken, B., Karssemeijer, N., Litjens, G., ... & Geessink, O. (2017). Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama, 318(22), 2199-2210.
