The use of synthetic data in medicine

Deep learning requires large, accurately labeled and well-balanced datasets to train AI models efficiently. Lack of datasets makes it necessary to collect and label data on one's own. However, in the case of a rare problem in reality, there is no source for acquiring real data.

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We introduce a new procedure of data augmentation - an intelligent algorithm for generating synthetic data based on intelligent image blending allows you to obtain any amount of data of the highest quality.

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Where can be used

Synthetic data can be used, among others, in the medical industry, drones, automotive, manufacturing or military industries. 

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Examples of the use of synthetic data in medicine:

• endoscopy
• histopathology
• tomography
• resonance

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Advantages of using our synthetic data:

• Solving the problem of unbalance and low data volume
• The input data is annotated by our experts
• We are able to generate any amount of synthetic data
• Accurate AI models training

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How it works

1. Unbalanced input data
2. The data labeling by our experts
3. Generation any amount of synthetic data based on input data using an intelligent algorithms
4. Balanced large dataset for accurate AI models training

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Results of usingthe algorithm in endoscopy

BioCam is a company established in May 2019 and based in Wrocław, Poland. We popularize patient-friendly examination of the whole gastrointestinal tract using our own capsule endoscopy platform, based on AI technology. We believe that with our solution, capsule endoscopy will be a go-to examination of the whole gastrointestinal tract.

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