Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
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 https://bit.ly/MICCAI-DEMI2023.