Consistency memory bank improves label free staining on large pathology slides
Seamless Whole Slide Label-Free Virtual Staining
Computer Vision and Pattern Recognition
Summary
Looking at tissue samples to detect diseases usually requires staining them with chemicals, which can be slow and harmful. The authors found a way to virtually stain large, high-resolution tissue images without using chemicals by using a new computer method called COMB. COMB keeps the image tiles smooth and consistent, avoiding visible seams or color shifts. This makes it easier to analyze tissue images and could help with tasks like identifying tumors.
What this means in practice
- •For pathology labs: Process large digitized tissue slides digitally with fewer visual artifacts to improve diagnostic workflows without using chemical stains.
- •For medical imaging software developers: Integrate a memory-efficient virtual staining method that maintains image consistency across tiles for improved whole slide image viewing and analysis.
Authors
Dou Hoon Kwark, Kianoush Falahkheirkhah, Ji-hun Oh, Shirui Luo, Volodymyr Kindratenko, Rohit Bhargava
Abstract
Label-free virtual staining offers a compelling, non-destructive alternative to standard histopathology; however, its clinical adoption is hindered by the computational bottlenecks inherent to processing gigapixel Whole Slide Images (WSIs). Current deep learning approaches require patch-based inference to avoid memory constraints, which disrupts global tissue continuity and introduces tiling artifacts--displaying visible seams and color shifts. To address this, we introduce the Consistency Memory Bank (COMB), a novel label-free virtual staining framework that enforces spatial and channel consistency across tiles without memory bottlenecks. COMB decouples context storage from computation, utilizing a dynamic retrieval mechanism to fetch feature representations from adjacent tiles. This enables a retrieval-based context integration strategy that adopts local padding to resolve spatial discontinuities and neighbor-aware channel attention to stabilize statistical drift. Further optimized with a sliding window schedule to ensure minimal memory overhead, our method demonstrates superior performance over state-of-the-art baselines, achieving significant improvements in both perceptual fidelity and tiling consistency, while suggesting its downstream utility in tumor segmentation. Code is available at https://github.com/dou0000/COMB.