why us

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TYPES OF PAST PROJECTS

Data curation:

separating usable from unusable data, identifying artifacts, data sampling to boost spatial resolution

Image segmentation for AI training:

pixel perfect and bounding box segmentation on CT, MRI, and plain films and digital pathology slides

Text to image correlation

Integration of existing software programs with ChatGPT

Skull removal for data processing/academic publication

3D models for education

Custom programming of new modules in 3D slicer

OUR ADVANTAGES

Expert Team with Broad Clinical and Annotation Experience

Our team consists of multi-national specialists with a strong clinical and scientific background across various fields.

Our biggest advantage is our real-world experience in medical data annotation, ensuring both accuracy and reliability in every project we undertake.

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Timeliness (Flexible Capacity)

We handle the labeling project and deliver the data on a timely basis so you can concentrate on your research and on developing new medical technologies.

We have flexible capacity in our workforce and therefore can expand quickly in order to meet a tight deadline.

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Availability of U.S. Radiologists and Pathologists

FDA approval requires that labelled images be reviewed by U.S. radiologists and pathologists, and we have U.S. specialists available on hand to support your project if required.

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Our team is proficient in using a wide range of annotation platforms and tools

We regularly conduct training to stay up-to-date with the latest technologies and methodologies.

Our biggest advantage is our real-world experience in medical data annotation, ensuring both accuracy and reliability in every project we undertake.

Our pathologists are trained in QuPath.

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IT specialists are ready to help

We have a team of experienced software engineers who can develop programs to better use your imaging data.

Our data scientists  can  do API integration and prompt engineering which was used while developing Smart 3D Slicer.Our team has developed custom extensions for 3D Slicer to enable features like: custom image processing pipelines (registration, segmentation, color manipulation), running large segmentation models on AWS, ad ChatGPT integration.

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Quality Control

We follow best-practice recommendations on medical image labeling for machine learning.  Our physicians' labelled images are internally reviewed and corrected before their work is presented to you for feedback.

Together, we will also review difficult-to-label edge cases to decide on best solutions. This way, our labeling will be accurate, predictable, and high quality.
Our data scientists will design programs to better improve the use of your data or create shortcuts for your programs.

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designed by @hopemsd

development by @blin4ik112