Making time for innovation with scalable image annotation
Overview
A medical AI company (MAI) turned to CloudFactory when its founder realized he needed to offload the burden of data labeling to focus on innovation. CloudFactory data analysts annotated databases of radiographs, identifying aging markers and signs of bone damage.
Services Used
Computer Vision Managed Workforce
Read the client story that follows, or download a PDF version you can reference later.
Industry
Healthcare
Headquarters
California, USA
Company Size
11-50
4,000+
Radiographs Tagged Per Month
2
Week Training Time to Full Capacity
2+
Year Partnership with CloudFactory
Meet Our Client
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.
Their 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.
“I was worried about crowdsourcing because I didn’t think the quality would be there. And some of the other companies I looked at had preconceived ways of working with clients,” MAI’s Founder explains, noting some were not interested in having staff work directly on his platform. In addition, he needed workers used to dealing with images because each batch is unique in terms of what is being tagged.
The space that I’m in is going through an evolution. If you don’t have AI capabilities, you’ll be left behind. But I can’t focus on the product if I’m swamped doing people management for image annotation. CloudFactory takes that burden off of us.
MAI Founder
Our 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, our client was a little concerned that data labelers without a medical background might not be successful, but he was pleasantly surprised. “I wrote some documents and recorded some videos. That’s all,’’ he says. “CloudFactory has done a great job. If something isn’t quite right, I give feedback and they get to the higher bar for the next set of data. They’ve also given us valuable feedback on our tool.’’
The altruistic aspect of CloudFactory’s approach also wooed MAI. “You’re on a mission to help people around the world that are good technologically but don’t have the opportunity. That was a big factor in my decision because we’re also trying to do the right thing.’’
The Results
CloudFactory data analysts were producing at full capacity within just two weeks and were self-sufficient after three months, giving MAI more time to focus on the go-to-market side of the business.
“The space that I’m in is going through an evolution. If you don’t have AI capabilities, you’ll be left behind. But I can’t focus on the product if I’m swamped doing people management. CloudFactory takes that burden off of us.’’
Note: This client/company requested to remain confidential; therefore real names were not used.
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