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

Area of Science:

• Digital Pathology
• Artificial Intelligence in Medicine
• Computational Pathology

Background:

• Whole Slide Image (WSI) analysis is vital for precision medicine, especially in oncology.
• Limited WSI dataset availability due to privacy concerns hinders deep learning model development and generalizability.
• Current data augmentation techniques often fall short in generating realistic and diverse pathological images.

Purpose of the Study:

• To propose an improved data augmentation method for Whole Slide Images (WSI) using Deep Convolutional Generative Adversarial Networks (DCGAN).
• To enhance the quality and diversity of synthetic WSI data for improved deep learning model training.
• To address the limitations imposed by privacy regulations on WSI dataset availability.

Main Methods:

• Leveraged self-supervised pretraining with CTransPath to extract rich WSI features for guiding synthetic image generation.
• Implemented an improved DCGAN incorporating least-squares adversarial loss and frequency domain loss for enhanced pixel accuracy and structural fidelity.
• Integrated residual blocks and skip connections to deepen the network, improve gradient flow, and stabilize training.

Main Results:

• The enhanced DCGAN achieved superior Structural Similarity Index (SSIM) and Fréchet Inception Distance (FID) scores compared to traditional models on the PatchCamelyon dataset.
• Augmented datasets generated by the proposed method significantly improved downstream classification task performance.
• Key performance metrics including accuracy, Area Under the Curve (AUC), and F1 scores were substantially enhanced.

Conclusions:

• The proposed improved DCGAN is effective for generating high-fidelity synthetic WSI data, overcoming limitations of data scarcity.
• This data augmentation strategy significantly boosts the performance and generalizability of deep learning models in digital pathology.
• The method holds promise for advancing precision oncology through more robust AI-driven WSI analysis.