Performance analysis of GANs for synthetic histopathology Image generation

The advent of Artificial Intelligence (AI) and Deep Learning (DL) techniques has revolutionized the field of medical image analysis, particularly in histopathology for cancer diagnosis. However, challenges persist, including the scarcity of annotated data for training robust models and the need for stain normalization to mitigate colour variations in histopathological images. This paper proposes a comprehensive approach leveraging Generative Adversarial Networks (GANs) to address these challenges. Specifically, it explores the use of GANs, Conditional GANs (CGANs), and Cyclic GANs for generating synthetic data and performing stain normalization. The study compares traditional methods with GAN-based approaches in terms of image quality metrics such as Fréchet Inception Distance (FID), Structural Similarity Index Metric (SSIM), Peak Signal-to-Noise Ratio (PSNR), Features Similarity Index Matrix (FSIM), and Root Mean Square Error (RMSE). Results indicate that while GANs struggle with unconditional image generation, CGANs and Cyclic GANs offer promising results comparable to traditional methods, even without paired image datasets. This research underscores the efficacy of GAN-based techniques, particularly CGANs, in addressing data scarcity and colour normalization challenges in histopathology image analysis, thus advancing the potential for earlier cancer diagnosis and treatment planning.