Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization

TL;DR

This paper introduces a novel framework combining contrastive learning-based multistage finetuning and reinforcement learning-based external optimization to improve GAN training stability and output quality in complex histopathology image synthesis.

Contribution

It presents a new integrated approach that enhances GAN performance by addressing training challenges with a contrastive multistage finetuning Siamese network and a reinforcement learning optimizer.

Findings

Outperforms state-of-the-art GAN models on histopathology image synthesis tasks.

Reduces mode collapse and improves image quality in high-resolution data.

Enhances feature extraction and training stability in complex domains.

Abstract

Generative Adversarial Networks (GANs) have been at the forefront of image synthesis, especially in medical fields like histopathology, where they help address challenges such as data scarcity, patient privacy, and class imbalance. However, several inherent and domain-specific issues remain. For GANs, training instability, mode collapse, and insufficient feedback from binary classification can undermine performance. These challenges are particularly pronounced with high-resolution histopathology images due to their complex feature representation and high spatial detail. In response to these challenges, this work proposes a novel framework integrating a contrastive learning-based Multistage Progressive Finetuning Siamese Neural Network (MFT-SNN) with a Reinforcement Learning-based External Optimizer (RL-EO). The MFT-SNN improves feature similarity extraction in histopathology data, while the RL-EO acts as a reward-based guide to balance GAN training, addressing mode collapse and enhancing output quality. The proposed approach is evaluated against state-of-the-art (SOTA) GAN models and demonstrates superior performance across multiple metrics.