Learned Image Compression and Restoration for Digital Pathology

L earned Image C ompressionand R estorationfor Digital Pathology Preprint, compiled A pril 2, 2025 SeonY eong Lee 1, EonSeung Seong 1, DongEon Lee 1, SiY eoul Lee 1, Y ubin Cho 1, Chunsu Park 1, Seonho Kim 1, MinKyung Seo 1, Y oungSin Ko 3, and MinWoo Kim 1,2,* 1 Department of Information Convergence Engineering, Pusan National University, Y angsan, Korea 2 School of Biomedical Convergence Engineering, Pusan National University, Y angsan, Korea 3 Seegene Medical Foundation, Seoul, Korea The first two authors contributed equally to this work. A bstract 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 e ffi ciency 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 e ffective 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 e fficient data management and long-term storage while ensuring seamless integration into clinical workflows and AI-assisted diagnostic systems. K eywords Learned Image Compression, Deep Learning, Wavelet Transform, Digital Pathology, Whole Slide Image. 1 I ntroduction Digital pathology images serve as fundamental data for various medical applications, playing a crucial role in cancer diagnosis, disease analysis, and treatment planning. These images are typically stored as Whole Slide Images (WSIs), which are characterized by ultra-high resolution (typically 0. 25µ m / px). A single uncompressed WSI can often exceed several gigabytes in size (e.g., 20-30 GB per image), posing significant challenges in terms of storage, transmission, and computational e ffi ciency.