Using a Pix2Pix conditional GAN to synthesise realistic fluorescence microscopy images — tackling the chronic scarcity of medical imaging data by generating high-quality training images from two distinct input types.
Medical imaging research is bottlenecked by data scarcity. Fluorescence microscopy datasets are expensive to produce and are often locked behind confidentiality constraints, leaving deep-learning models under-trained and hard to validate. Building on the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, this project asked whether generative AI could responsibly expand these datasets without compromising patient confidentiality.
Design and evaluate a generative pipeline that produces high-quality synthetic fluorescence images from two different input types, and validate whether a newly published white-blood-cell microscopy dataset is reliable enough for supervised learning.