AAI_2025_Capstone_Chronicles_Combined

Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018).

GAN-based synthetic medical image augmentation for increased CNN performance

in liver lesion classification. Neurocomputing , 321, 321–331.

https://doi.org/10.1016/j.neucom.2018.09.013

Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative

adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer

Vision and Pattern Recognition (pp. 4401–4410).

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and

improving the image quality of StyleGAN. In Proceedings of the IEEE/CVF

Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8110–8119).

IEEE. https://doi.org/10.1109/CVPR42600.2020.00813

Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., & Aila, T. (2021).

Alias-free generative adversarial networks [White paper]. NVIDIA. https://nvlabs-fi

cdn.nvidia.com/stylegan3/stylegan3-paper.pdf

Kermany, D., Zhang, K., & Goldbaum, M. (2018). Labeled Optical Coherence Tomography

(OCT) and Chest X-Ray Images for Classification (Version 2) [Data set]. Mendeley

Data. https://doi.org/10.17632/rscbjbr9sj.2

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L.,

McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M. K., Pei, J., Ting, M. Y. L.,

Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., … Zhang, K. (2018). Identifying medical

122