Learned Image Compression and Restoration for Digital Pathology

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.

MicroMIL: Graph-based Contextual Multiple Instance Learning for Patient Diagnosis Using Microscopy Images

Current histopathology research has primarily focused on using whole-slide images (WSIs) produced by scanners with weakly-supervised multiple instance learning (MIL). However, WSIs are costly, memory-intensive, and require extensive analysis time. As an alternative, microscopy-based analysis offers cost and memory efficiency, though microscopy images face issues with unknown absolute positions and redundant images due to multiple captures from the subjective perspectives of pathologists. To this end, we introduce MicroMIL, a weakly-supervised MIL framework specifically built to address these challenges by dynamically clustering images using deep cluster embedding (DCE) and Gumbel Softmax for representative image extraction. Graph edges are then constructed from the upper triangular similarity matrix, with nodes connected to their most similar neighbors, and a graph neural network (GNN) is utilized to capture local and diverse areas of contextual information. Unlike existing graph-based MIL methods designed for WSIs that require absolute positions, MicroMIL efficiently handles the graph edges without this need. Extensive evaluations on real-world colon cancer (Seegene) and public BreakHis datasets demonstrate that MicroMIL outperforms state-of-the-art (SOTA) methods, offering a robust and efficient solution for patient diagnosis using microscopy images. The code is available at https://anonymous.4open.science/r/MicroMIL-6C7C