Medical Image annotation for future healthcare

Medical Image annotation for future healthcare

Rather than other industry healthcare sector is totally different. It is on high priority sector all of us expect more care and services without considering the cost. Most of the interpretations of medical data are being done by the medical expert. After the success of deep learning in other real-world application, it is providing exciting solutions with good accuracy for medical imaging and is seen as a powerful method for future applications in the health sector.


Deep Learning has achieved precedent success in computer vision and other areas. And now it is drastically changing the world of medicine. AI helps doctors diagnoses faster with precise quality and accuracy. IBM researchers estimate that medical images, fastest-growing data and the largest source in the healthcare industry, account for at least 90 % of all medical data.

Listed below are the applications of AI in the medical field

  •         Managing Medical Records and Other Data
  •         Medical Imaging Analysis and Pattern Recognition
  •         Radiology-Diagnostic assessment and reporting on X-Rays, CT Scans, MRIs
  •         Pathology – Assist pathologists in making rapid and accurate diagnoses
  •         Dermatology – Skin Image Analysis & Personalized Treatment
  •         Ophthalmology – Early detection of high-impact diseases like Glaucoma 
  •         Digital Consultation and Precision Medicine
  •         Treatment Design and Medication Management
  •         Health Monitoring and Virtual Patient Care
  •         Disease Management and Clinical Trials
  •         Drug Creation and Healthcare System Analysis

Now how these computer vision models are processed? The computer vision models are fed with hundreds of medical images with labelled regions showing the affected region. These images serve as a base for the model that trains the machine to detect diseases using the ML algorithms.

Automatic organ detection and segmentation play a huge role in medical imaging applications. For instance, in the cardiac analysis, automatic segmentation of the heart chambers is used for cardiac volume and ejection fraction calculation. One main challenge in this field is the lack of data and annotations. Specifically, medical imaging annotations have to be performed by clinical experts, which is costly and time-consuming

At this stage, it is necessary to hire professionals providing medical image annotation services to annotate medical images with the highest accuracy on AI-based models. Infolks is providing medical imaging data sets with annotation service to help users to build machine learning data sets with the highest quality and maximum accuracy.


  • Help customers by 100% data security.
  • Promising high-quality work
  • Strictly time bounded
  • Teamwork by highly skilled workers
  • Providing dual level quality checking
  • Dedicated project and quality managers.
  • The lowest hourly rate in the market
  • Ready to do free job samples
  • Payment only after complete satisfaction
  • 3 years of experience in annotation works
  • Able to handle large and small data
  • 24*7 custom shift services available

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