Privacy-Preserving Data Augmentation for Digital Pathology Using Improved DCGAN.

Abstract

The intelligent analysis of Whole Slide Images (WSI) in digital pathology is critical for advancing precision medicine, particularly in oncology. However, the availability of WSI datasets is often limited by privacy regulations, which constrains the performance and generalizability of deep learning models. To address this challenge, this paper proposes an improved data augmentation method based on Deep Convolutional Generative Adversarial Network (DCGAN). Our approach leverages self-supervised pretraining with the CTransPath model to extract diverse and representationally rich WSI features, which guide the generation of high-quality synthetic images. We further enhance the model by introducing a least-squares adversarial loss and a frequency domain loss to improve pixel-level accuracy and structural fidelity, while incorporating residual blocks and skip connections to increase network depth, mitigate gradient vanishing, and improve training stability. Experimental results on the PatchCamelyon dataset demonstrate that our improved DCGAN achieves superior SSIM and FID scores compared to traditional models. The augmented datasets significantly enhance the performance of downstream classification tasks, improving accuracy, AUC, and F1 scores.