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Our experience in medical field
With Infolks, get perfectly labeled data for training smart and intelligent medical applications. We have set up a dedicated team of experts exclusively for medical data annotation.
Our experience in medical field
Our experience in medical field
We analyse scanned retinal images(eye cells) for symptoms of ocular diseases and label them using bounding boxes. Some of our industrial experience include:
With our services, you can label microscopic images of cells used in the detection and analysis of diseases. We label nuclei fluorescence images, cell fluorescence images, phase-contrast images and UV images using contour annotation. We have annotated microscopic images of:
Diagnostic imaging like X-ray, CT & MRI scan eases the diagnosis and treatment of illness. We analyse this generated imaging and label specific disease symptoms using diverse annotation techniques. We have annotated:
We annotate images of teeth using semantic segmentation. Tooths are labeled using some unique ids.
Our Other MedTech Cases
Our Other MedTech Cases
With our data labeling services, you get accurately labeled data for training smart systems used in liver diagnosis. We analyse CT images and label different liver disease cases using various annotation techniques.
We analyse scanned images of kidneys and do exact annotations in it by labeling kidney stones using various annotation techniques. These data are used for training systems applied in early detection and identification of kidney stones.
Brain disease diagnosis is performed by analyzing scanned images. We do associated annotations needed to train and evaluate ML models that can analyze MRI images and detect tumour structures.
Our experts can analyze the scanned images and label microscopic images of cancerous cells using diverse annotation techniques.
Skin may vary in color, texture, local structure or other characteristics. We annotate needed structures by analyzing skin biopsy images using various annotation techniques.
Medical records can be huge ranging from patients personal details to their medical histories. We accurately label them to process quality training data for training computer vision and NLP systems used in medical data handling.