Medical algorithms

Save your time and scale your business with us.

Benefit from our scientists, business analysts, bioengineers, annotators, software and ML/DL engineers. We have experience with projects of varying complexity and based on different types of medical data.

Medical algorithms

Save your time and scale your business with us.

Benefit from our scientists, business analysts, bioengineers, annotators, software and ML/DL engineers. We have experience with projects of varying complexity and based on different types of medical data.

Medical algorithms development

Machine Learning in healthcare

Our proven Machine Learning pipeline assures efficient and fast development of AI algorithms – both machine learning and deep learning ones.

Deep Learning in healthcare

We are experienced in applying deep learning techniques to various tasks, including medical imaging analysis, and clinical decision support, or drug discovery.

Ground-truth preparation

Our experts established a ground-truth preparation process. Additionally, we can support you with an annotating team as well as cohort data optimisation.

Performance

We carefully listen to our clients and jointly determine the method for measuring algorithm performance, aiming for generalization and bias-free models.

Full pipeline for ML processing

Check our complete pipeline for ML-based data processing, which allows us to create tailored solutions for our partners more quickly and efficiently.

Expertise in diverse data

Although our primary area of interest is medical image analysis, in our success stories we have projects based on other data as well as vision analysis.

Medical algorithms development

Machine Learning in healthcare

Deep Learning in healthcare

Ground-truth preparation

Our proven Machine Learning pipeline assures efficient and fast development of AI algorithms – both machine learning and deep learning ones.

We are experienced in applying deep learning techniques to various tasks, including medical imaging analysis, and clinical decision support, or drug discovery.

Our experts established a ground-truth preparation process. Additionally, we can support you with an annotating team as well as cohort data optimisation.

Performance

Full pipeline for ML processing

Expertise in diverse data

We carefully listen to our clients and jointly determine the method for measuring algorithm performance, aiming for generalization and bias-free models.

Check our complete pipeline for ML-based data processing, which allows us to create tailored solutions for our partners more quickly and efficiently.

Although our primary area of interest is medical image analysis, in our success stories we have projects based on other data as well as vision analysis.

Our healthcare algorithms development pipelines get your project covered

Machine Learning medical project lifecycle

1

Medical images

2

Data storage

3

Manual annotations

4

Dataset configuration

5

Model preparation

6

Models repository

7

Inference results

How we work on medical algorithm development

Shape. To start, we’ll make sure we completely grasp your requirements. Secondly, we’ll map out how we’ll collaborate. Our experts will also identify the key solution components and the deliverables needed for certification.

Integrate and verify. Once the various elements of the solution have been made ready and tested in isolation, they are assembled and end-to-end verification takes place through formal trials.

Create. Usually, we consider several network types to establish algorithms. Initially, data will be used to train the algorithm. Then, additional data will be used to test and refine the algorithm.

Implement. After implementation, algorithms can still be tested and refined based on actual data. If needed, your algorithm can be refined in the future.

How we work on medical algorithm development

Shape. To start, we’ll make sure we completely grasp your requirements. Secondly, we’ll map out how we’ll collaborate. Our experts will also identify the key solution components and the deliverables needed for certification.

Integrate and verify. Once the various elements of the solution have been made ready and tested in isolation, they are assembled and end-to-end verification takes place through formal trials.

Create. Usually, we consider several network types to establish algorithms. Initially, data will be used to train the algorithm. Then, additional data will be used to test and refine the algorithm.

Implement. After implementation, algorithms can still be tested and refined based on actual data. If needed, your algorithm can be refined in the future.

Let’s work on your challenges together!

Let’s work on your challenges together!

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