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.

翻译：数字病理学图像在医学诊断中发挥着至关重要的作用，但其超高分辨率和大文件尺寸对存储、传输和实时可视化提出了重大挑战。为解决这些问题，我们提出了CLERIC，一种专为全切片图像（WSIs）设计的新型基于深度学习的图像压缩框架。CLERIC集成了可学习的提升方案和先进的卷积技术，以在保留关键病理细节的同时提升压缩效率。我们的框架在分析阶段采用提升方案变换，将图像分解为低频和高频分量，从而实现更具结构性的潜在表示。这些分量通过包含可变形残差块（DRB）和循环残差块（R2B）的并行编码器进行处理，以改进特征提取和空间适应性。合成阶段应用逆提升变换进行有效的图像重建，确保细粒度组织结构的高保真复原。我们在数字病理学图像数据集上评估CLERIC，并将其性能与最先进的学习型图像压缩（LIC）模型进行比较。实验结果表明，CLERIC实现了优异的率失真（RD）性能，在保持高诊断图像质量的同时显著降低了存储需求。我们的研究凸显了基于深度学习的压缩在数字病理学中的潜力，有助于实现高效的数据管理和长期存储，同时确保其能无缝集成到临床工作流程和AI辅助诊断系统中。代码和模型可在以下网址获取：https://github.com/pnu-amilab/CLERIC。