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

Osama Mustafa

TL;DR

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) is proposed, which improves feature similarity extraction in histopathology data and acts as a reward-based guide to balance GAN training, addressing mode collapse and enhancing output quality.

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.

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

TL;DR

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) is proposed, which improves feature similarity extraction in histopathology data and acts as a reward-based guide to balance GAN training, addressing mode collapse and enhancing output quality.

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.
Paper Structure (26 sections, 11 equations, 14 figures, 4 tables)

This paper contains 26 sections, 11 equations, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. MFT-SNN: Multistage Finetuning based Siamese Neural Network
  4. Feature Extractor and Training Objective
  5. Proposed Training Strategy
  6. Mathematical Formulation for MFT-SNN Training
  7. Reinforcement Learning-Based External Optimizer (RL-EO)
  8. RL-EO Output as Reward Signal to Discriminator
  9. Modified Discriminator Loss Function
  10. Reward Interpretation
  11. Base GAN
  12. Experimental Protocol
  13. Dataset
  14. Data Preparation
  15. Data for MFT-SNN and Modified GAN
  16. ...and 11 more sections

Figures (14)

  • Figure 1: A block diagram of MFT-SNN.
  • Figure 2: A standard Siamese Neural Network dey2017signet.
  • Figure 3: A flow diagram of our complete proposed solution including MFT-SNN and RL-EO.
  • Figure 4: I - A line plot showing reward maximization as training progresses, with reward value on y-axis.
  • Figure 5: II - A line plot showing reward maximization as training progresses, with reward value on y-axis.
  • ...and 9 more figures