IRS：用于数字病理学的增量关系引导分割
IRS: Incremental Relationship-guided Segmentation for Digital Pathology
AI总结：
提出增量关系引导分割（IRS）学习方案，通过增量通用命题矩阵建模解剖关系，解决数字病理学中时间序列部分标注数据的全景分割问题并保持OOD持续学习能力。
摘要：
持续学习正迅速成为计算机视觉的关键焦点，旨在开发能够持续改进的AI系统，从而提升其在各种现实应用中的价值和实用性。在医疗保健领域，持续学习对于每天在医院收集的持续获取的数字病理数据具有巨大潜力。然而，对数字全切片图像（WSIs）进行全景分割提出了重大挑战，因为通常无法为所有潜在对象获取全面的标注，这些对象涵盖从粗粒度结构（例如区域和单元对象）到细粒度结构（例如细胞）。这导致了时间序列上的部分标注数据，对开发整体分割框架构成了主要挑战。此外，理想的分割模型应纳入新的表型、未见过的疾病和多样化的人群，这使得该任务更加复杂。在本文中，我们引入了一种新颖且统一的增量关系引导分割（IRS）学习方案，以处理时间序列获取的部分标注数据，同时在数字病理学中保持分布外（OOD）持续学习能力。IRS的关键创新在于，它通过一个简单的增量通用命题矩阵对现有类别和新引入类别之间的解剖关系进行数学建模，从而实现了一种新的时空OOD持续学习范式。实验结果表明，IRS方法有效处理了病理分割的多尺度特性，能够在多种放大倍数下实现跨各种结构（区域、单元和细胞）以及OOD疾病病灶的精确肾脏分割。这种能力显著增强了领域泛化能力，使IRS成为现实世界数字病理学应用的稳健方法。
英文摘要：
Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practicality in diverse real-world applications. In healthcare, continual learning holds great promise for continuously acquired digital pathology data, which is collected in hospitals on a daily basis. However, panoramic segmentation on digital whole slide images (WSIs) presents significant challenges, as it is often infeasible to obtain comprehensive annotations for all potential objects, spanning from coarse structures (e.g., regions and unit objects) to fine structures (e.g., cells). This results in temporally and partially annotated data, posing a major challenge in developing a holistic segmentation framework. Moreover, an ideal segmentation model should incorporate new phenotypes, unseen diseases, and diverse populations, making this task even more complex. In this paper, we introduce a novel and unified Incremental Relationship-guided Segmentation (IRS) learning scheme to address temporally acquired, partially annotated data while maintaining out-of-distribution (OOD) continual learning capacity in digital pathology. The key innovation of IRS lies in its ability to realize a new spatial-temporal OOD continual learning paradigm by mathematically modeling anatomical relationships between existing and newly introduced classes through a simple incremental universal proposition matrix. Experimental results demonstrate that the IRS method effectively handles the multi-scale nature of pathological segmentation, enabling precise kidney segmentation across various structures (regions, units, and cells) as well as OOD disease lesions at multiple magnifications. This capability significantly enhances domain generalization, making IRS a robust approach for real-world digital pathology applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
