Healthcare

Fortunately, these days we see continuous development of diagnostic and treatment tools available in hospitals and clinics. High-definition scanners allow radiologists to recognize anomalies more reliably and efficiently. However, the healthcare industry is always facing challenges. Our group works on solutions that help the whole industry take a big step forward: we work on automating the process of detecting the anomaly itself. Modern computer vision together with deep-learning models is already capable of seeing objects on radiology images and marking them out automatically. Such computer-aided diagnosis systems help doctors in analysis of medical images, increasing reliability and reducing workload.

What We Do

Microscopic Image Segmentation And Pathology Recognition

New methods of automated recognition of pathology patterns in biopsy and blood smear slides significantly enhance the efficiency and accuracy of ...

MRI Segmentation

Machine learning and computer vision techniques allow for performance of automatic segmentation of MRI and extraction of volumetric and shape data ...

Computer-Aided Diagnostic Sonography

An ultrasound computer-aided diagnosis system processes images, highlighting abnormalities in textures and shapes, assisting radiologists in the ...

Diagnosis Decision Support Systems

An advanced clinical decision support system provides clinicians with the probabilities of possible death on the basis of patients’ symptoms and ...

Our Vision

Although hospitals are still in the early stages of using computer vision systems, we believe that computer-aided diagnosis will revolutionize the healthcare industry. For many diseases, a crucial aspect is the stage when diseases can be detected and treatment started. For example, if cancer is detected at an early stage, with proper treatment, patients have a very high survival rate. However, currently, access to radiologists who can detect cancer is still very limited, or even unavailable because of lack of specialized physicians. The only way to make regular access to screening affordable is by using automated or semi-automated detecting solutions that can be handled by a large number of clinicians. There is no doubt that this equipment will be used all over the world in the next few decades.

In addition to computer vision systems, pure machine-learning algorithms find applications in the healthcare industry. Hospitals and researchers are interested in refining large datasets of medical records and genomic data to gain insights into therapies and drugs. Our company is proud to offer technologies that can help doctors improve our health.

Why BitRefine

We are as excited about computer science as we are about biology. Thus, the intersection of these two scientific areas is always exceptionally interesting for us.

Like most tasks in medical image processing, automated anomaly detection requires knowledge of both the morphology of the selected organ and the machine-learning models. Our specialists have experience in both fields.
A variety of tasks can be carried out using computer vision and deep-learning technologies, meaning that every project starts with R&D. BitRefine group offers a complete spectrum of services from consultancy to post-deployment support.