Detection and Labeling of Dermatological Phenomena by Data Annotators without any Medical Background

In order to develop a smart dermatoscope that uses Artificial Intelligence to automatiсally detect skin pathologies, our client needed to label huge datasets. These datasets included images showing five different pathologies: pigment networks, negative networks, streaks, milia-like cysts, and globules. Our goal was to prove that annotators without a medical background could identify and label the right pathologies.

Our client produces innovative healthcare devices, the development of which involves lots of manual data labeling. The type of data to be labeled made the situation more complicated, as labeling needed to be done by professional dermatologists. In order to help our client optimize labor costs and get the labeling job done, Mindy Support created a team of eight experienced annotators, one medical supervisor, and a project manager.

Before our annotators started the job, our medical supervisor provided training materials and a short tutorial. However, it was still challenging for them to mark the pathologies quickly and with maximum accuracy. In order to deal with all of the issues that arose, Mindy Support decided to expand the training process:

After this process, the annotators were ready to process bigger datasets of 500, 1,000, and 10,000+ images quickly and accurately.

All the annotated data was checked by the medical supervisor before delivery to the client.

The annotation team: