Your focus—saving lives
our focus—faster cancer detection

Faster cancer detection in Cancer Center

Our multidisciplinary group of researchers and clinicians (including medical physics, biology, oncology, biomedical engineering, mathematics and computer science) are combining their skills to investigate the best solutions for faster cancer detection and grading.

State-of-the-art technology gives us the chance to improve human health conditions substantially. To clarify, with the use of various imaging techniques, the human body and tissues can be examined in great detail which in turn makes it possible to analyze internal phenomena. As a result, well educated and experienced doctors can give a diagnosis at the most specific and detailed level.

However, there is one fundamental problem. Time.

Fortunately, there is also a solution. Well educated and experienced people who can make doctors work easier, faster, and much more efficient.



But first I want to introduce you to Jakob


Jakob is our true inspiration. He was lucky to win his battle with cancer. Because of him, we have learned first hand how inefficient and slow the process of cancer diagnosis is. This is to say, in cancer diagnosis and therapy, the decision making process needs the ​combined opinion of pathologists, radiologists, geneticists and oncologists. In addition, the amount of data is huge and the overall decision process to make the final conclusion takes a lot of time. Likewise, shortage of experienced oncologists is a worldwide problem. The same is with radiologists, pathologists and geneticists. Being engineers and entrepreneurs, we decided to do something about it.

We can provide doctors with all the necessary tools to work with medical images. To clarify, we connect the new technology of medical imaging and the most advanced and futuristic solutions from the field of mathematics and computer science (machine and deep learning). Cancer Center applies deep learning techniques to the field of oncology/radiology. A huge training set of medical images along with categorization technology allow computers to predict multiple diseases with better-than-human accuracy.

Cancer Center Story

In 2015 Piotr Krajewski and the group of skilled scientists from Wroclaw University of Science and Technology won MICCAI (Medical Image Computing and Computer Assisted).

Their exceptional expertise in artificial intelligence and machine learning was once again granted in 2016. They won competition in the Combined Radiology and Pathology Classification twice over (Classification based on images of radiological and histological examinations).

After these successes Piotr Krajewski wanted to do something more. His aim was to help both doctors and patients in speeding up and improving cancer diagnosis. That’s why in February 2017 they created company Cancer Center.

Since then, we have developed our products: PathoPlatform, Radiology Platform and SkinChecker. We have established cooperation with doctors (urologists, pathologists, radiologists, dermatologists) from hospitals, medical centers and medical institutes. We are still looking for new contacts and solutions to improve our tools.

Our Mission – faster cancer detection


Our mission is to combine state-of-the-art computer science research with medical imaging. In order to make the world of cancer treatment a better place we need to make use of the technology to the fullest. In other words, using advanced machine learning techniques in medical image diagnosis on the regular basis is the next big leap that our society needs to make in order to progress. Certainly, our tools facilitate this transformation.

Benefits for patients:

  • Better access to the second and third cancer diagnosis from doctors directly to patients
  • Easier and quicker access to oncology experts
  • Lower appointment costs (no transport or accommodation costs)
  • Getting a faster and more accurate diagnosis – patients would be able to commence the appropriate treatment sooner

Benefits for doctors:

  • Shorter time for preparing diagnosis,
  • Support in the image analysis process resulting in a better quality and higher number of diagnoses,
  • Easier access to other oncology experts to confirm the diagnosis,
  • Find similar cases (knowledge base),
  • Cooperation with other oncology experts (share experience)
  • More time to focus on truly challenging cases