In the rapidly evolving field of medical imaging, machine learning algorithms
have become indispensable for enhancing diagnostic accuracy. However, the
effectiveness of these algorithms is contingent upon the availability and
organization of high-quality medical imaging datasets. Traditional Digital
Imaging and Communications in Medicine (DICOM) data management systems are
inadequate for handling the scale and complexity of data required to be
facilitated in machine learning algorithms. This paper introduces an innovative
data curation tool, developed as part of the Kaapana open-source toolkit, aimed
at streamlining the organization, management, and processing of large-scale
medical imaging datasets. The tool is specifically tailored to meet the needs
of radiologists and machine learning researchers. It incorporates advanced
search, auto-annotation and efficient tagging functionalities for improved data
curation. Additionally, the tool facilitates quality control and review,
enabling researchers to validate image and segmentation quality in large
datasets. It also plays a critical role in uncovering potential biases in
datasets by aggregating and visualizing metadata, which is essential for
developing robust machine learning models. Furthermore, Kaapana is integrated
within the Radiological Cooperative Network (RACOON), a pioneering initiative
aimed at creating a comprehensive national infrastructure for the aggregation,
transmission, and consolidation of radiological data across all university
clinics throughout Germany. A supplementary video showcasing the tool's
functionalities can be accessed at this https URL

传统 DICOM 数据管理系统无法处理机器学习算法所需的数据规模和复杂性，本论文介绍了一种创新的数据整理工具，旨在简化大规模医学影像数据集的组织、管理和处理，满足放射科医生和机器学习研究人员的需要，并在 RACOON 下进行集成。