Healthcare

Modernizing the healthcare industry with the highest quality training data for AI to build the most robust and fast ML pipelines.

computer vision in healthcare

Use cases

computer vision in healthcare

X-ray annotation

Train machines to analyze X-ray images for automated fracture or issue detection and even treatment projection.
computer vision in healthcare

Cancer cell detection

Label scanned images and microscopic images of cancerous cells to identify cancer cells and enhance cancer prevention means.
computer vision in healthcare

Medical record documentation

Simplify medical data handling and cut down the resources spent on recording and locating documentation due to precisely labeled patient details and medical histories.
computer vision in healthcare

Teeth and gum segmentation

Leverage our incompatible semantic segmentation tool to annotate teeth images and take dentistry to the next level.
computer vision in healthcare

Eye cell analysis

Analyze scanned retinal images for symptoms of ocular diseases and label them accurately with bounding boxes.
computer vision in healthcare

Microscopic Cell Analysis

Label microscopic images of cells using our pixel-cut polyline annotation tool to contribute to disease detection and analysis.

How Altris AI achieved a 12% increase in model accuracy

Altris AI is a deep learning platform for real-time innovative ophthalmology diagnosis that uses SuperAnnotate to manage their in-house team of annotators-ophthalmologists. In addition to key metrics such as annotation speed, cost, pixel-cut annotation quality is the top priority for Altris AI and what they do. Prior to SuperAnnotate, Altris AI have been using another annotation platform.

Read more about their achievements with our platform and learn the reasons behind switching to SuperAnnotate.

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altris ai case study superannotate
computer vision in healthcare

SuperAnnotate for healthcare

Machine learning completely remodels the healthcare industry and provides tools to detect and prevent diseases –something no human could do before.

Annotation quality is especially critical when it comes to healthcare. Training this kind of large, complex model requires a robust data annotation workflow with quality management measures and smooth iteration cycles.

SuperAnnotate is designed to feed quality data into AI models and get them into production up to 5x faster.

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