RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation

Marcos Roberto Nesso Junior, M. Cazzolato, L. C. Scabora, Paulo H. Oliveira, Gabriel Spadon, J. D. Souza, Willian D. Oliveira, D. Y. T. Chino, J. F. Rodrigues, A. Traina, C. Traina
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引用次数: 5

Abstract

Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.
RAFIKI:基于检索的影像与知识调查应用
医学检查,如CT扫描和乳房x光检查,每天都在世界各地的医院获得和存储,包括图像、患者数据和医疗报告。有工具和系统来改进基于如此海量存储信息的计算机辅助诊断是至关重要的。基于内容的图像检索(CBIR)是帮助实现这一目标的强大范例,为医生提供智能检索工具,向他/她展示相似或互补的病例,其中视觉特征改善了文本数据。通过对以往病例的比较检查,医生可以对他/她正在处理的病例有更全面的了解。目前的医院系统还不具备原生的CBIR功能,依赖于附加的子系统,这些子系统通常不遵循现有的关系数据库基础结构。在这项工作中,我们提出了RAFIKI,一个扩展关系数据库管理系统(RDBMS) PostgreSQL的软件原型,为CBIR功能提供本机支持,模块化可扩展性,以及数据科学工具(如Python和r)的无缝集成。我们通过评估三个临床场景,对真实世界的肺部检查图像数据集执行查询来展示我们系统的适用性。我们的研究结果从医生的角度发现了促进知情决策的实际潜力。此外,与文献中发现的系统相比,该系统表现出更高的性能。此外,RAFIKI还提供了一个模型来建立如何将CBIR概念和相关数据组合在一起,为进一步开发理论和实践概念和工具提供了强大的设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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