This repository houses the code and documentation for a research project that explores the application of Generative Adversarial Networks (GANs) in medical imaging, specifically focusing on pathology datasets. The primary objective is to leverage GANs to synthesize nuclear detection datasets, overcoming the challenges posed by Health Insurance Portability and Accountability Act (HIPAA) regulations restricting access to real patient data. The synthetic datasets generated aim to facilitate the training of nuclei detection models, ultimately contributing to the detection of cancerous cells.

• Aravind Dendukuri (ardend)
• Atharva Shah (athshah)
• Maharshi Gor (magor)
• Subhranil Das (dassubh)

The research project involves the exploration of various GAN architectures, including DCGAN, Variational Autoencoders, and StyleGAN3. The final step involves training a YOLOv8 architecture to create a labeled dataset, which is crucial for nuclei detection model development. The synthetic datasets, derived from consenting patient data, address the data scarcity challenge imposed by HIPAA regulations.

• Pathology Datasets
• Generative Adversarial Networks (GANs)
• Medical Imaging
• Synthetic Data
• Nuclear Detection
• Health Insurance Portability and Accountability Act (HIPAA)
• Cancer Detection
• DCGAN
• Variational Autoencoders
• StyleGAN3
• YOLOv8 Architecture