MEDICAL AI COMPANY (MAI) Making time for innovation with scalable image annotation
Company Size
11-200
Region
- America
Country
- United States
Product
- CloudFactory Managed Workforce
- MAI Custom Annotation Tool
Tech Stack
- Machine Learning
- Computer Vision
- Semantic Segmentation
- 3D Point Cloud
- Sensor Fusion Systems
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Computer Vision Software
- Analytics & Modeling - Machine Learning
- Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Computer Vision
- Predictive Maintenance
- Remote Asset Management
Services
- Data Science Services
- System Integration
About The Customer
MAI provides an image database for research and evidence-based healthcare and needed a way to label thousands of images quickly and accurately to further its product offering. Managing in-house staff, even on a contract basis, was time-consuming and expensive. But the image files are big and the task is complex, so outsourcing the task seemed daunting as well. Within months of choosing CloudFactory’s managed workforce option, MAI completed 12 additional imagery databases to improve its product and is sold on our managed workforce process.
The Challenge
MAI needed the labeling work done by a consistent group of individuals who could log on remotely to the company’s own annotation tool which is custom-designed with machine learning components built-in. The work is critical to MAI’s efforts to stay ahead of the curve in providing AI-based image databases that enhance medical professionals’ understanding of health issues. One of its goals is to provide predictive advice based on tagging and analyzing images throughout a patient’s lifespan in order to enhance preventative care. MAI’s owner researched multiple companies looking for the best image tagging help. He was worried about crowdsourcing because he didn’t think the quality would be there. Some companies had preconceived ways of working with clients and were not interested in having staff work directly on his platform. Additionally, he needed workers used to dealing with images because each batch is unique in terms of what is being tagged.
The Solution
MAI chose CloudFactory for its flexibility and its experience working with other computer vision companies. CloudFactory workers are trained in a wide array of use cases from bounding boxes and semantic segmentation to 3D point cloud and sensor fusion systems. In addition, CloudFactory has a set of best practices developed from annotating millions of images and videos over the last ten years. Initially, MAI was concerned that data labelers without a medical background might not be successful, but he was pleasantly surprised. He provided some documents and recorded videos for training. CloudFactory has done a great job, and if something isn’t quite right, feedback is given and improvements are made. CloudFactory has also provided valuable feedback on MAI's tool.
Operational Impact
Quantitative Benefit
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