URICA：面向病理图像任意区域检索的均匀性区域仿射标识符捕获算法
URICA: A Uniformity Region Affine Identifier Capture Algorithm for Arbitrary Region Retrieval in Pathology Images
把「任意朝向、任意尺度的区域」当作检索单元，用语义镶嵌（semantic tessellation）把图像块组织成几何感知的区域描述子，并给出其与像素级语义掩码目标之间的理论误差界——这套「区域描述子 + 仿射不变匹配」的思路可直接迁移到遥感、医学等大图上的任意区域检索与跨尺度匹配任务。
原文依据 we redefine WSI retrieval as a semantically optimal matching problem between arbitrary regions under spatial transformations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论全切片图像（WSI）的区域检索在计算病理学中仍是一个未解决的难题，因为现有方法难以表示并保留所有可能区域的信息。当前依赖固定尺寸图像块或切片级检索的方法与真实临床工作流并不匹配——病理学家通常检查的是任意朝向、任意尺寸的 WSI 区域，而非预定义的图像块或整张切片。在本工作中，我们将 WSI 检索重新定义为空间变换下任意区域之间的语义最优匹配问题，这要求一种能够保持语义一致性的区域级表征。为满足这一要求，我们引入语义镶嵌（semantic tessellation），它将图像块单元组织为灵活的、几何感知的区域描述子。基于该表征，我们进一步提出仿射标识符（affine identifier），这是一种语义签名，能够实现旋转与尺度一致的区域匹配。我们还推导了镶嵌所得描述子与理想像素级语义掩码目标之间的理论界，表明它们能够可靠地逼近基于掩码的区域相似度。这些组件共同构成 URICA，一种具有理论基础的鲁棒 WSI 区域检索算法。在大型公开数据集上的实验表明，URICA 在多种 WSI 检索任务上均取得强劲且一致的性能。
英文原文摘要 1396 字符
Whole slide image (WSI) region retrieval remains an open challenge in computational pathology, as existing methods struggle to represent and preserve information of all possible regions. Current approaches that rely on fixed-size patches or slide-level retrieval are misaligned with real clinical workflows, where pathologists often examine WSI regions of arbitrary orientations and sizes rather than predefined patches or slides. In this work, we redefine WSI retrieval as a semantically optimal matching problem between arbitrary regions under spatial transformations, which necessitates a region-level representation that maintains semantic consistency. To fulfill this requirement, we introduce semantic tessellation, which organizes patch units into flexible, geometry-aware region descriptors. Building on this representation, we develop the affine identifier, a semantic signature that enables rotation- and scale-consistent region matching. We further derive theoretical bounds between the tessellation-derived descriptors and the ideal pixel-level semantic mask objective, showing that they reliably approximate mask-based region similarity. Together, these components form URICA, a theoretically grounded algorithm for robust WSI region retrieval. Experiments on large public datasets demonstrate that URICA achieves strong and consistent performance across diverse WSI retrieval tasks.