Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752 ms. The source code is available at https://github.com/zhengyushan/lagenet.

近年来，基于内容的组织病理学图像检索（CBHIR）在组织病理学图像分析中变得流行。CBHIR 系统通过从预先建立的数据库中搜索并返回与感兴趣区域 (ROI) 内容相似的区域，为病理学家提供辅助诊断信息。从由组织病理学全幻灯片图像 (WSI) 组成的数据库中检索诊断相关区域在临床应用中具有挑战性且意义重大。在本文中，我们提出了一种基于位置感知图和深度散列技术从 WSI 数据库中检索区域的新框架。与目前的 CBHIR 框架相比，WSI 中 ROI 的结构信息和全局位置信息都通过图卷积和自注意力操作保留，这使得检索框架对组织分布相似的区域更加敏感。此外，受益于图结构，所提出的框架对于 ROI 的大小和形状变化具有良好的可扩展性。它允许病理学家根据组织的外观使用自由曲线定义查询区域。第三，基于散列技术实现检索，保证了该框架对于实际的大型WSI数据库的高效性和适用性。所提出的方法在具有 2650 个 WSI 的内部子宫内膜数据集和公共 ACDC-LungHP 数据集上进行了评估。实验结果表明，在不规则区域检索任务中，所提出的方法在子宫内膜数据集上的平均精度在 0.667 以上，在 ACDC-LungHP 数据集上的平均精度在 0.869 以上，优于最先进的方法。从包含 1855 个 WSI 的数据库中检索的平均时间为 0.752 毫秒。源代码可在 https://github.com/zhengyushan/lagenet 获得。