The company is among the pioneers offering these services to a wide range of clients, including hospitals, research institutes, and industry, from startups to scaleups to large international companies. We fill the gap between purely academic research and building commercially available end-products. Let’s collaborate.

Our primary solution is image segmentation. Segmentation is the contouring of objects or labeling of pixels in the image and is at the heart of almost all clinical applications. Segmentation is also at the heart of almost all commercially available products. Applications include improved visualization of anatomy, measurements for treatment devices, detection and characterization of pathology, surgical planning and navigation, and many more.

Why XDMD?

XDMD has a team of research scientists and software engineers, each excelling in at least one, and often in multiple domains. We all love to program, also the people in leadership position. XDMD has an advisory board of leading scientists world-renowned in their field.

XDMD has in-depth understanding of the full imaging pipeline – from acquisition to high-level interpretation. We have broad knowledge of the available software libraries and have in-depth understanding of the latest developments in AI. We maintain a live review of methodology and can write reviews for your clinical application.

XDMD has decades of combined experience in academia and industry. We have worked in several academic hospitals and understand clinical context.

XDMD has a large network of leading professionals world-renowned in machine learning, medical imaging, and several clinical domains. All are highly esteemed colleagues with whom we have collaborated previously in different settings and projects, and many are friends.

XDMD <> academia

  • Focus on segmentation
  • Focus on what works
  • Publish only if needed
  • Long-term continuity of expertise and experience
  • Multi-center data, enabling building foundation models
  • Access to professional GPU cluster
  • Collaboration based on shared values and objectives
  • Focus on scientific or career advancements (publishing papers, getting PhD, getting tenure)
  • Focus on novelty
  • You must publish
  • Expertise tied to PhD students with long learning curve and fixed-term contracts
  • Easier access to data but often single-center only
  • Often limited computational resources
  • Strong competition within academia

XDMD <> industry

  • Organization is small and flexible, communication fast and direct
  • Doing R&D
  • Solution is part of shared objective
  • In-depth understanding of medical imaging, decades of combined experience
  • Organization is large, more layers for decision making
  • Working on end-products
  • Solution not always possible because not part of product line
  • Expertise and experience dependent on few individuals within smaller team