Expertise in Medical AI Image and Video Data Annotation

Sidney Hammer

Annotating images and videos with medical AI data helps us build, deploy, and optimize high-quality computer vision ML models. Research and discovery in totalmedicalimaging.com and pharmaceutical fields, robotics and surgical devices, and clinical and diagnostic imaging are among the fields we structure training data for. Grouping The method can be […]

Annotating images and videos with medical AI data helps us build, deploy, and optimize high-quality computer vision ML models. Research and discovery in totalmedicalimaging.com and pharmaceutical fields, robotics and surgical devices, and clinical and diagnostic imaging are among the fields we structure training data for.

Grouping

The method can be used in a variety of ways to analyze the content of images – from CT scans to microscopy images to MRIs and X-rays – in order to determine how objects within an image are the same or different.

It can also be used to identify changes over time. Surgical images can be segmented at the pixel level, including tissue, instruments, needles, threads, etc.

Boxes of bounding

In most cases, they are used to draw a box around an object, especially when the target object is symmetrical, such as lungs, kidneys, and other anatomical features. Likewise, it is used when the shape of the object is less important or when occlusion is less of a concern.

Two-dimensional and three-dimensional bounding boxes are both possible. Three-dimensional bounding boxes are also referred to as cuboids. We often see bounding box and polygon techniques used in conjunction with “masking” in medical AI – a pixel-level annotation used to hide areas in an image and to reveal other areas of interest, which make it easy to focus on specific parts of a medical image.

Places of interest

Using this method, for example with facial recognition, you can detect facial features, emotions, and expressions in the data. Using pose-point annotations, it is also used to annotate body position and alignment.

As an example, when you annotate images for surgical robotics, you can determine where the surgeon’s hand, wrist, and surgical devices are in relation to one another as they perform the procedure.

Learn about CloudFactory

CloudFactory provides a scalable, expertly trained human-in-the-loop (HITL) workforce for accelerating AI initiatives in healthcare, life sciences, and pharmaceuticals. The managed workforce trained in medical AI tasks, combined with our tooling agnostic approach, offers the data security, scale, and quality to support computer vision AI/ML development, deliver product innovations, and enhance customer satisfaction.

The AI-trained surgery robot improves patient outcomes and reduces costs

By integrating computer vision and robotics, this robotics company improves patient outcomes and reduces healthcare system costs. This robotics company provides advanced imaging and diagnostics during surgery. Surgery robots gather images of surgical procedures which are segmented by our team. Labeling images gathered by surgery robots for objects such as tissue, instruments, needles, and thread.

  • A 97{f9a9d0c078b99c5035c80896b16d2b484abb346d07724230e395ff1864e1808b}+ accuracy rate, exceeding the target
  • Annotations at the pixel level of multiple objects
  • CloudFactory has partnered with us for 2 years

 

 

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