Healthcare Services

Annotation of all types of medical images and data.

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Medical Data Annotation Services We Provide

Mindy Support has established operations to process machine learning healthcare data with the highest quality and accuracy. Our curated training data sets are helping companies develop cuttingedge AI-based healthcare applications and algorithms. Each medical project is differentiated by the type of examination, pathology, labelling tool, level of expertise required etc. We take a flexible approach and work closely with each client.

Types of Medical Images and Data

Our employees can annotate all types of medical images and data quickly and accurately.

  • MRIs

  • CT Scans

  • Mammogram

  • PET scans

  • X-Rays

We provide not only medical image annotation services but also descriptions and medical assessment reports for medical screenings.

What We Can Do

  • Accurately define pathologies or double-check radiological reports.

  • Provide data labelling services (boxes, polygons, semantic segmentation, etc.) for X-rays, CT scans, MRIs, tomomovies, PET scans, and other medical images (using DCOMs).

  • Analyze patient X-rays, MRIs, CT and PET scans, and other medical data.

  • Provide medical insights and feedback to help with tools’ design that support deep learning products and services.

Potential For Growth

Medical education in Ukraine is considered among the best in the world. Ukrainian specialists in the medical and pharmaceutical fields are prepared at:

  • 14

    Medical universities and academies;

  • 47

    Medical universities and academies;

  • 3

    Academies of postgraduate education.

Human Resources

Mindy Support has more than 1,000 data annotation specialists, including 100+ medical data annotation professionals and 50+ medical practitioners (qualified radiologists, pathologists, surgeons, dermatologists, and general physicians) that offer multilingual support.
  • 1,000+ data annotation specialists

  • 100+ medical data annotation professionals

  • 50+ medical practitioners

Let’s Collaborate

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Mindy Support cooperates with state medical research institutes, national medical universities, national medical institutions, and private clinics.

Our dedicated HR department is able to recruit any medical specialist upon request. Depending on client’s needs, we can form different types of medical data annotation teams.

  • Team of interns with university degrees in medical sciences but minimal practical experience under the supervision of a medical practitioner.

  • Team of people trained to process medical data based on clients’ instructions. These teams work under the supervision of a team leader with a medical degree.

  • Team of medical professionals with 3+ years of practice in the field of interest.

Tools We Use For Medical Image Processing

  • Our professionals work with almost all existing DCOM viewers and annotation tools.

  • RadiAnt, Horos, 3d Slicer, ITK-SNAP, ImageJ, Three Palm, etc.

  • Work in clients’ and partners’ tools to ensure security and privacy.

  • We can annotate images in our web application with smart markup features and present outputs in a format suitable for you.

Сase Study

Labeling Biopsied Lesions in DBT Movies

Our client from California is developing investigational physics-based artificial intelligence and deep machine learning solutions to help radiologists get more accurate readings of breast images. Mindy Support team annotated with bounding boxes approximately 4,000 mammograms with 2D and 3D biopsy studies that were extracted from multiple clinical sites. Mindy Support helped the client to save 70% of costs reaching 98% accuracy.

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Сase Study

Temporal Bone Structure Segmentation by a Team of Otolaryngologists

Our client from the USA conducts research in the medical field to study, predict, and prevent different types of pathologies. Mindy Support team of otolaryngologists created a temporal bone structure segmentation. As a result, 500 CT scans were annotated. Mindy Support helped the client to save 84% of costs reaching 99% accuracy of segmentation.

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