The application of deep learning in cancer research, particularly in early diagnosis, case understanding, and treatment strategy design, emphasizes the need for high-quality data. Generative AI, especially Generative Adversarial Networks (GANs), has emerged as a leading solution to challenges like class imbalance, robust learning, and model training, while addressing issues stemming from patient privacy and the scarcity of real data. Despite their promise, GANs face several challenges, both inherent and specific to histopathology data. Inherent issues include training imbalance, mode collapse, linear learning from insufficient discriminator feedback, and hard boundary convergence due to stringent feedback. Histopathology data presents a unique challenge with its complex representation, high spatial resolution, and multiscale features. To address these challenges, we propose a framework consisting of two components. First, we introduce a contrastive learning-based Multistage Progressive Finetuning Siamese Neural Network (MFT-SNN) for assessing the similarity between histopathology patches. Second, we implement a Reinforcement Learning-based External Optimizer (RL-EO) within the GAN training loop, serving as a reward signal generator. The modified discriminator loss function incorporates a weighted reward, guiding the GAN to maximize this reward while minimizing loss. This approach offers an external optimization guide to the discriminator, preventing generator overfitting and ensuring smooth convergence. Our proposed solution has been benchmarked against state-of-the-art (SOTA) GANs and a Denoising Diffusion Probabilistic model, outperforming previous SOTA across various metrics, including FID score, KID score, Perceptual Path Length, and downstream classification tasks.

翻译：深度学习在癌症研究中的应用，特别是在早期诊断、病例理解及治疗策略设计方面，突显了对高质量数据的需求。生成式人工智能，尤其是生成对抗网络（GANs），已成为解决类别不平衡、鲁棒学习与模型训练等挑战，并应对患者隐私和真实数据稀缺问题的领先方案。尽管前景广阔，GANs仍面临诸多挑战，包括其固有缺陷以及组织病理学数据特有的问题。固有挑战包括训练不平衡、模式崩溃、因判别器反馈不足导致的线性学习，以及严格反馈引发的硬边界收敛。组织病理学数据因其复杂表征、高空间分辨率及多尺度特征而带来独特挑战。为应对这些挑战，我们提出了一个包含两个组件的框架。首先，我们引入一种基于对比学习的多阶段渐进微调孪生神经网络（MFT-SNN），用于评估组织病理学图像块之间的相似性。其次，我们在GAN训练循环中实现了基于强化学习的外部优化器（RL-EO），作为奖励信号生成器。改进后的判别器损失函数引入了加权奖励，引导GAN在最小化损失的同时最大化该奖励。该方法为判别器提供了外部优化指导，防止生成器过拟合并确保平滑收敛。我们提出的解决方案已在当前最先进的GANs及去噪扩散概率模型上进行了基准测试，在包括FID分数、KID分数、感知路径长度及下游分类任务在内的多项指标上均超越了先前的最优结果。