AI Platform

Better&Faster Cancer Diagnosis =  Medical Images + Deep Learning

Our mission

Cancer Center applies deep learning techniques to the field of oncology/radiology. It has amassed a huge training set of medical images along with categorization technology that will allow computers to predict multiple diseases with better-than-human accuracy. Our solutions (API and web platform) is to offer better access to the second and third diagnosis of cancer to medical professionals and directly to patients by providing a data exchange platform stuffed with machine learning algorithms that speed up and improve the accuracy of the medical image analyses.


Don’t waste your time! Let us do the work! We can segment images, find regions of interest, provide statistical descriptions of images, count cells, mitosis or even recognize their kind.

Don’t hesitate, just save lives.


  • Machine Learning / Deep Learning
  • Automated Medical Image Analysis
  • MRI (Magnetic Resonance Imaging)
  • PET / CT
  • Pathology Images (Whole Slide Imaging)
  • General planimetry

MRI, DICOM Analysis

It is one of the most important and most profitable diagnostic tools. Not only this medical imaging modality is not invasive but also can be applied in many different scenarios, obtaining images of pretty much every part of the human body. In case of the prostate cancer, our software/deep learning algorithms can be used to find Region of Interest (ROI), cancer segmentation automatically.


Digital Pathology

In terms of precision, there are no better methods of seeing a single tissue as… to examine the tissue under the microscope. Every histopathologist would agree that sometimes finding the most descriptive part of the slide is a harder task than it is to make the diagnosis itself. Fortunately it is not a problem for us. We can localize the most important parts of the slide, what is more we can extract the very factors that influence diagnostic models the most.

PET Analysis

In oncology, Positron Emission Tomography (PET) imaging is widely used in diagnostics of cancer metastases, in monitoring of progress in course of the cancer treatment, and in planning radiotherapeutic interventions. Accurate and reproducible delineation of the tumor in the PET scans remains a difficult task, despite being crucial for delivering appropriate radiation dose, minimizing adverse side-effects of the therapy, and reliable evaluation of treatment. Our aim is to provide clinicians with intelligent software supporting accurate, efficient and reproducible delineation of the tumor.

Last Posts

Cancer Center are going to the MICCAI 2016 in Athens

We are goint to Athens and present our results in challenges: PETSEG: PET Segmentation Challenge Using a Data Management and Processing Infrastructure The PETSEG challenge will provide a comparative study of a range of state-of-the-art algorithms for PET image...

Symposium in Wrocław

Since few years the commission of biomedical engineering of Wrocław PAN have been organizing science meeting “Sympozjum Współczesna Myśl Techniczna w Naukach Medycznych i Biologicznych” (Symposium Modern Think of Technology in Medical and Biological Sciences). Current...


Scientific Conference: Patient’s Safety and Health Care Professionals Management – International Experience PIKMED is a pan regional organisational structure. It is a partnership between corporations operating in the medical and health protection sectors. This...

Workshop in Dresden

Our team have participated in workshop in Dresden in the field of innovative solutions in the courtyard of medical imaging. We have presented solutions and further ideas. We had the opportunity to listen to many experts and learn about other innovative solutions. We...

Again in media – Teleexpress and TVP Wroclaw, Polsat News, eska

Again in media – Teleexpress and TVP Wroclaw, Polsat News, eska After the great victory in Munich, in the MICCAI competition, more and more often we hear about us in media. All thanks to a system recognizing and diagnosing changes in the brain. The inspiration to...

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DCO Onology cancer center

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