Business Challenge
Situation
A leading medical device manufacturer required a state-of-the-art AI platform to handle computer vision use cases involving large-scale processing of medical imaging data. The primary focus was on automating organ segmentation in the human body using advanced machine learning techniques. The challenges included:
Data Volume
Managing a massive influx of DICOM images, including X-rays and CT scans.
Accuracy Needs
Developing models for auto-segmentation of over 50 anatomical structures in CT scans, requiring high precision.
Data Governance
Ensuring compliance with regional data sovereignty laws as images could not be transferred between regions.
Global Presence
Supporting multiple offices worldwide (e.g., India, China, US) with a unified yet region-specific AI infrastructure.
Task
The goal was to create a distributed AI platform that:
Enabled federated learning to train global models while respecting regional data constraints.
Automated and accelerated the development of machine learning models using V-Net architecture.
Provided scalable, reliable infrastructure to support massive data volumes and computational workloads.
Solution
Action
The team developed a state-of-the-art distributed AI platform to meet the customer’s requirements, leveraging advanced machine learning and federated learning techniques. Key actions included:
Federated Learning
Implemented a distributed learning approach to train global models while keeping data localized in each region, ensuring compliance with data sovereignty regulations.
V-Net Architecture
Built machine learning models based on V-Net for auto-segmentation of over 50 structures in human CT scans, achieving exceptional accuracy.
Distributed Infrastructure
Established region-specific AI platforms across multiple locations (India, China, US), enabling seamless collaboration and model development.
Kubeflow Ecosystem
Deployed the AI platform on Azure cloud, leveraging the Kubeflow 1.0 ecosystem, including KServe for serving machine learning models.
Custom Image Catalog
Designed a custom-built image catalog for efficient discovery and management of DICOM images.
Cloud-Native Platform
Deployed a container-native infrastructure capable of scaling compute and storage resources dynamically.
Metadata Cataloging
Developed a catalog for searching AI assets such as models, parameters, and hyperparameters, ensuring reproducibility and traceability.
Key Highlights
The goal was to create a distributed AI platform that:
Processed large-scale DICOM image datasets for X-rays and CT scans.
Enabled federated learning to ensure localized data processing and global model training.
Automated and optimized workflows to support rapid model iteration and deployment.
Leveraged advanced GPU performance monitoring and container scaling solutions.
Business Outcomes
Results
The distributed AI platform delivered transformative results:
Global Scalability
Enabled federated learning across three regions, facilitating collaboration without moving sensitive data.
Operational Efficiency
Reduced model development times by leveraging automation and distributed resources.
Accuracy and Impact
Delivered industry-leading models for organ auto-segmentation with a dice coefficient of 0.9.
Performance Gains
Reduced data processing times for terabyte-scale datasets from 48 hours to 10-12 hours.
Capacity and Reliability
Supported over one petabyte of medical imaging data, ensuring future-ready scalability.
Forward-Thinking Value
Positioned the customer as a leader in leveraging distributed AI for healthcare innovations.
Enhanced global collaboration while maintaining strict adherence to data sovereignty laws.
Established a foundation for integrating advanced AI solutions and expanding into new medical imaging use cases.
Technologies & Tools
AI Models
V-Net architecture for organ segmentation.
Distributed Learning
Federated learning techniques for global model training.
Data Processing
Cloud-native pipelines for managing DICOM images.
Platform Ecosystem
Kubeflow 1.0 for machine learning workflows, KServe for model serving.
Image Management
Custom-built image catalog for discovering and managing DICOM images.
Monitoring and Optimization
GPU performance monitoring and real-time dashboards.
Infrastructure
Kubernetes and Terraform for scalable, container-native deployment.
Security and Governance
Role-based access control and metadata cataloging for traceability and compliance.
This innovative approach transformed the customer’s operations, enabling them to lead in the global healthcare AI landscape while maintaining compliance and achieving exceptional results.