Create datasets for diagnostics on medical imaging in the standard DICOM format.
Locate, mark, and label regions of interest on medical images such as X-Rays
Polygon masks
Text labels
Bounding boxes
Class-based color
Create AI-backed solutions to detect and identify anomalies with a deep and more accurate analysis
Cancer detection
Recognizing abnormal regins for new image data, then predicting whether the patient is healthy or has cancer.
Dental health assessment
Detecting all teeth related problems such as tooth decay or cavities.
Increasing data quality might boost the performance of the decision-making core of your AI-backed system
Identify appropriate class labels
Use specific class labels to tag the objects of interest in each image.
Box size diversity
Use different box sizes to train the model on various sized objects.
Class color customization
