Building scalable ML pipelines for radiology image analysis in cloud environments

Radiology image analysis is one of the essential fields of machine learning (ML) application because of the dynamic growth of medical imaging data, and the need to observe fast and accurate results of diagnostics. Yet, the development of scalable ML pipelines that would be able to process and analyze mass radiological data is associated with significant challenges. These are data heterogeneity, computational-intensity, regulatory-compliance and scalability of deployment. In this paper, an end-to-end methodology is introduced to design and develop scalable ML pipeline to analyze radiology images with the help of cloud architecture. We take advantage of cloud-native services as data ingestion, preprocessing, training, deployment, and continuous monitoring. As our experiments carried out on chest X-ray and MRI datasets show, our technology is faster, more accurate, and more scalable than the conventional on-premise systems. The paper emphasizes the need of modular pipeline design, serverless architectures, container orchestration when it comes to real-time troubleshooting and model lifecycle management. It can be strongly stated that the given manner of approach is extremely flexible in terms of different types of radiology and ML building blocks, so it is a viable solution to contemporary healthcare AI systems.