Enabling medical research and AI through high quality data labelling
Common Problems with Medical Data Labelling
Prototyping new ideas is slow
Scaling up products is difficult
Expensive regulatory approvals
Unreliable quality
Slow and difficult communication
Inability to deal with complexity
Network of trained medical experts
high quality & reliable data
Easily scalable
from small to big projects
Robust quality assurance
helps regulatory approvals
Expertise
in complex 3D segmentations
Seamless communication
projects managed by medical experts
Highly flexible
fast adaptability & labels on demand
David Golan
Co-founder, Viz.ai
"Viz derived great value from Labelata's work. It allowed us to jumpstart a project much faster than we would have otherwise and probably shaved 1-2 months off our delivery timeline which is significant. Not only was the speed and quality of the segmentations impressive, we particularly liked the ease of communication with the Labelata team."
SERVICES
Labelata specialises in supporting academics, firms and hospitals with Medical research and AI projects
DATA LABELLING
We ensure quality and protect your data
Provide manual segmentation and labelling of any anatomical and/or pathological structures across all imaging modalities
(e.g. MRI, CT, US etc.)
CONSULTING & QUALITY ASSURANCE
Technical advice & expertise from experienced radiologists
Our network of radiologists with experience in AI projects provide quality assurance and can offer help and guidance to plan your project
DATA ACQUISITION
Acquire training data
We acquire the training data you need to perform research and development
CASE STUDIES
How to scale up and commercialise research and development
Labelata has supported the Spine Biomechanics Group to publish multiple clinical studies and provided the comfort and reliability to scale their products. They have now created a spin-off to commercialise their research.
Only available in English
How to develop FDA-cleared algorithms to analyse medical imaging data
Viz.ai was able to launch the Left Ventricle/Right Ventricle ratio algorithm (an FDA approved AI algorithm). Labelata’s segmentations of the heart helped Viz.ai develop an algorithm that automatically calculates the ratio between the two ventricles, a critical measurement to evaluate the severity of pulmonary embolism.
Only available in English
How to establish ground truth for Medical AI models?
Labelata's team of highly skilled musculoskeletal experts helped Covera Health to produce ground truth. Using this training data, Covera was able to build AI models to develop software providing quality insights on Musculoskeletal ('MSK') radiology.
Only available in English
CLIENTS
ETH COMPUTER VISION LAB
UNIVERSITY OF ZURICH
