Nearest neighbor search (NNS) aims to locate the points in high-dimensional space that is closest to the query point. The brute-force approach for finding the nearest neighbor becomes computationally infeasible when the number of points is large. The NNS has multiple applications in medicine, such as searching large medical imaging databases, disease classification, diagnosis, etc. With a focus on medical imaging, this paper proposes DenseLinkSearch an effective and efficient algorithm that searches and retrieves the relevant images from heterogeneous sources of medical images. Towards this, given a medical database, the proposed algorithm builds the index that consists of pre-computed links of each point in the database. The search algorithm utilizes the index to efficiently traverse the database in search of the nearest neighbor. We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets. The proposed approach outperformed the existing approach in terms of retrieving accurate neighbors and retrieval speed. We also explore the role of medical image feature representation in content-based medical image retrieval tasks. We propose a Transformer-based feature representation technique that outperformed the existing pre-trained Transformer approach on CLEF 2011 medical image retrieval task. The source code of our experiments are available at https://github.com/deepaknlp/DLS.
翻译:近邻搜索( NNS) 旨在定位距离查询点最近的高维空间的点。 查找最近邻居的粗力方法在数量较大时就变得计算不可行。 NNS在医学上有多种应用, 如搜索大型医疗成像数据库、疾病分类、诊断等。 以医疗成像为重点,本文件建议 DenseLinkSearch 是一种有效且高效的算法, 搜索和检索来自不同医学图像来源的相关图像。 为此, 根据一个医疗数据库, 提议的算法构建了包含数据库中每个点预先计算链接的索引。 搜索算法利用该索引高效率地绕过数据库搜索最近的邻居。 我们广泛测试了拟议的NNS 方法, 并将该方法与在基准数据集和我们创建的医疗图像数据集方面的最新NNS 方法进行比较。 拟议的方法在重新定位准确邻居和检索速度方面超越了现有方法。 我们还在基于内容的医学图案/ DRS 检索任务中探索了医学图象显示的作用。 我们提议在现有的C- L 变式任务前的校样代码 。 我们提议了一种基于2011 格式的校验 CLF 格式 。