How Data Infrastructure Deals with Bias Problems in Medical Imaging

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Data Science

Background:

• Medical imaging analysis faces significant challenges due to biases in machine learning algorithms and generative models.
• Existing approaches often struggle to address these biases effectively, impacting the reliability of AI-driven diagnostics.
• The development of robust data infrastructure is crucial for mitigating bias and ensuring ethical AI deployment in healthcare.

Purpose of the Study:

• To introduce a taxonomy of bias problems in medical imaging analysis.
• To propose a solution framework for addressing bias within a standardized data infrastructure.
• To evaluate the effectiveness of generative models for data augmentation while ensuring privacy and FAIR data principles.

Main Methods:

• Development of a bias taxonomy for medical imaging data.
• Implementation of the Platform for Analytics and Distributed Machine-Learning for Enterprises (PADME) infrastructure.
• Utilizing generative models for data augmentation and privacy-preserving image transfer.
• Experimental validation of the proposed solution framework within the PADME environment.

Main Results:

• Generative methods demonstrate effectiveness in augmenting medical imaging datasets.
• The proposed framework successfully addresses bias at different data stations.
• Privacy is preserved during image transfer within the PADME infrastructure.
• The PADME initiative facilitates structured and privacy-preserving health data sharing.

Conclusions:

• Standardized data infrastructure, like PADME, is essential for mitigating biases in medical imaging AI.
• Generative models can be leveraged for effective data augmentation in healthcare.
• Adherence to FAIR principles and privacy preservation are critical for trustworthy AI research environments.
• The proposed framework offers a scalable solution for bias mitigation and privacy preservation in medical imaging analysis.