Distributed Deep Learning for Medical Image Denoising with Data Obfuscation

TL;DR

This paper demonstrates that distributed deep learning with optimized training strategies significantly accelerates medical image denoising while maintaining high quality, using lightweight obfuscation for privacy and comparing U-Net architectures.

Contribution

It introduces a distributed training pipeline with software optimizations for medical image denoising, comparing U-Net and U-Net++ architectures under various configurations.

Findings

U-Net++ outperforms U-Net in structural fidelity and perceptual similarity.

Optimized training reduces time by over 60%, outperforming standard methods.

Lightweight Gaussian noise effectively obfuscates sensitive data without degrading denoising quality.

Abstract

Medical image denoising is essential for improving image quality while minimizing the exposure of sensitive information, particularly when working with large-scale clinical datasets. This study explores distributed deep learning for denoising chest X-ray images from the NIH Chest X-ray14 dataset, using additive Gaussian noise as a lightweight obfuscation technique. We implement and evaluate U-Net and U-Net++ architectures under single-GPU, standard multi-GPU (DataParallel), and optimized multi-GPU training configurations using PyTorch's DistributedDataParallel (DDP) and Automatic Mixed Precision (AMP). Our results show that U-Net++ consistently delivers superior denoising performance, achieving competitive Peak Signal to Noise Ratio (PSNR) and Structured Similarity Index Method (SSIM) scores, though with less performance in Learned Perceptual Image Patch Similarity (LPIPS) compared to U-Net under low and moderate noise levels. This indicates U-Net++'s enhanced structural fidelity and low perceptual similarity. Meanwhile, our optimized training pipeline reduces training time by over 60% for both models compared to single-GPU training, and outperforms standard DataParallel by over 40%, with only a minor accuracy drop for both models (trading some accuracy for speed). These findings highlight the effectiveness of software-level optimization in distributed learning for medical imaging. This work demonstrates the practical viability of combining architectural design, lightweight obfuscation, and advanced distributed training strategies to accelerate and enhance medical image processing pipelines in real-world clinical and research environments. The full implementation is publicly available at: https://github.com/Suadey/medical-image-denoising-ddp.