Our radiologists are practicing clinicians who also have years of experience in labeling and are very familiar with 3D-Slicer and other custom platforms.
Our radiology team has experience working with CT, MRI, plain film and angiograms.
Our pathologists are currently practicing physicians who also have years of experience labeling. Our pathology team has used custom platforms and are very familiar with QuPath.
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.
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.
We handle the labeling project and deliver the data on a timely basis so you can concentrate on your research and developing new medical technologies.
We have flexible capacity in our workforce and therefore can expand quickly in order to meet a tight deadline.
FDA approval requires that labelled images be reviewed by U.S. radiologists and pathologists, and we have U.S. radiologists and pathologists available should that need arise for your project.
Our company takes care of project coordination, training, payments tor
radiologists, pathologists and data scientists and taxes. We navigate the compliance and bureaucracy issues in other countries so your company won't have to.
We understand that the field of Artificial Intelligence in medical imaging is slow to progress because of the lack of data and the cost of labeling work. We proposed to change the cost consideration by outsourcing the labeling work to our team of international radiologists, pathologists and data scientists.