无缝全切片无标记虚拟染色
Seamless Whole Slide Label-Free Virtual Staining
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- National Center for Supercomputing Applications（国家超级计算应用中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对全切片无标记虚拟染色中的平铺伪影和内存瓶颈，提出一致性记忆库（COMB）框架，通过动态检索和上下文整合实现无缝染色，显著提升感知保真度与一致性。
AI中文摘要：
无标记虚拟染色为标准组织病理学提供了一种引人注目的非破坏性替代方案；然而，其在临床上的应用受到处理吉像素级全切片图像（WSIs）所固有的计算瓶颈的阻碍。当前的深度学习方法需要基于补丁的推理以避免内存限制，这破坏了全局组织连续性并引入平铺伪影——显示可见的接缝和颜色偏移。为解决这一问题，我们引入了一致性记忆库（COMB），一种新颖的无标记虚拟染色框架，它在无内存瓶颈的情况下强制跨平铺的空间和通道一致性。COMB将上下文存储与计算解耦，利用动态检索机制从相邻平铺获取特征表示。这使得基于检索的上下文整合策略得以实现，该策略采用局部填充来解决空间不连续性，并采用邻域感知的通道注意力来稳定统计漂移。通过滑动窗口调度进一步优化以确保最小的内存开销，我们的方法在感知保真度和平铺一致性方面均显著优于最先进的基线方法，同时表明其在肿瘤分割中的下游实用性。代码可在该https URL获取。
英文摘要：
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.
