Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications

TL;DR

This paper presents a new data curation tool within the Kaapana toolkit designed to efficiently organize, manage, and validate large-scale medical imaging datasets for machine learning, addressing current limitations in data handling and bias detection.

Contribution

The paper introduces an innovative, open-source data curation tool tailored for medical imaging datasets, enhancing organization, quality control, and bias detection for machine learning applications.

Findings

Improved dataset organization and management functionalities.

Enhanced quality control and validation processes.

Ability to identify dataset biases through metadata analysis.

Abstract

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.