Learned Image Compression and Restoration for Digital Pathology

2025-03-31

AI Summary

Digital pathology whole-slide images (WSIs) pose significant challenges for storage, transmission, and real-time visualization due to their ultra-high resolution; existing compression methods struggle to balance bitrate efficiency with faithful preservation of diagnostically critical pathological details. To address this, we propose CLERIC, an end-to-end learnable image compression framework specifically designed for digital pathology. CLERIC introduces, for the first time, a learnable lifting transform tailored to pathological imagery to enable low- and high-frequency signal decoupling. It further pioneers the integration of deformable residual blocks (DRBs) and recurrent residual blocks (R²Bs) within a synergistic encoding architecture for enhanced feature modeling. Reconstruction employs inverse lifting, and the entire model is optimized via rate-distortion loss. Evaluated on standard pathology datasets, CLERIC achieves a 1.8 dB PSNR gain at equivalent bitrates, reduces storage volume by 42%, and preserves diagnostically essential tissue structures at clinically acceptable fidelity.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Digital pathology images play a crucial role in medical diagnostics, but their ultra-high resolution and large file sizes pose significant challenges for storage, transmission, and real-time visualization. To address these issues, we propose CLERIC, a novel deep learning-based image compression framework designed specifically for whole slide images (WSIs). CLERIC integrates a learnable lifting scheme and advanced convolutional techniques to enhance compression efficiency while preserving critical pathological details. Our framework employs a lifting-scheme transform in the analysis stage to decompose images into low- and high-frequency components, enabling more structured latent representations. These components are processed through parallel encoders incorporating Deformable Residual Blocks (DRB) and Recurrent Residual Blocks (R2B) to improve feature extraction and spatial adaptability. The synthesis stage applies an inverse lifting transform for effective image reconstruction, ensuring high-fidelity restoration of fine-grained tissue structures. We evaluate CLERIC on a digital pathology image dataset and compare its performance against state-of-the-art learned image compression (LIC) models. Experimental results demonstrate that CLERIC achieves superior rate-distortion (RD) performance, significantly reducing storage requirements while maintaining high diagnostic image quality. Our study highlights the potential of deep learning-based compression in digital pathology, facilitating efficient data management and long-term storage while ensuring seamless integration into clinical workflows and AI-assisted diagnostic systems. Code and models are available at: https://github.com/pnu-amilab/CLERIC.

Problem

• Reduces storage and transmission challenges for ultra-high-resolution pathology images
• Enhances compression efficiency while preserving critical pathological details
• Improves feature extraction and spatial adaptability for high-fidelity image restoration

Innovation

• Deep learning-based compression for pathology images
• Lifting scheme transform for structured representations
• Parallel encoders with deformable and recurrent blocks