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We are on the threshold of the fourth industrial revolution, which is based on the development of artificial intelligence. AI advances also provide an opportunity to improve medical diagnostics. There are currently too few doctors to respond to the needs of an aging society. As a result, specialists are increasingly overworked and access to them is increasingly limited. How can artificial intelligence support doctors in their daily work? Can dedicated solutions improve the diagnosis of diseases and cancer? These are the main research areas of our team.

Our goal is to shape the future of medical diagnostics by developing AI-based solutions. In our research, we focus on the analysis of medical imaging data, such as radiological and histopathological data (digital pathology).

Over the past 30 years, the number of cancer cases has more than doubled and cancer is one of the most common causes of premature death in highly and moderately developed countries. Quick and effective diagnostics, based on radiological and histopathological tests, is of key importance in the fight against cancer. New technologies, the dynamic development of AI and computer vision, and the increase in computational capabilities have contributed to significant progress in digital medical diagnostics. Currently, we can use complex algorithms to detect and segment cancerous areas, classify the occurrence of the disease and predict the response to treatment. These capabilities allow us to support the medical diagnostic process, reducing physician burden, diagnosis time and costs. At the same time, we can track the development and complexity of the disease.

The research we conduct is interdisciplinary and combines areas such as machine learning, biology and medicine. We work closely with medical units and doctors to develop new algorithms supporting medical diagnostics and to better understand the development of cancer diseases.

 

Research Team Leader


Żaneta Świderska-Chadaj

Professor Żaneta Świderska-Chadaj is an expert in the biomedical and biological image analysis domain, leader of the Medical Pathology Diagnostics research team at IDEAS NCBR. Her research is of an application and interdisciplinary nature, combining fields such as deep learning, machine learning and vision, biology, and medicine. She is a professor at Warsaw University of Technology, where she conducts research in the biomedical area. She was a postdoc in the Computational Pathology Group at Radboud, the Netherlands (2017-2020) and  Visiting Research at Cedar-Sinai Medical Center, the USA in 2019, and at Castilla La Mancha University, Spain in 2017. She is a laureate of the Start FNP scholarship, the Minister’s Scholarship for Outstanding Young Scientists, and the Polityka Science Award.

Other research groups and teams

  • Medical Pathology Diagnostics Our goal is to shape the future of medical diagnostics by developing AI-based solutions. We focus on the analysis of medical imaging data, such as radiological and histopathological data (digital pathology).
    Żaneta Świderska-Chadaj
  • Precision Forestry The use of remote sensing data in obtaining information about forests has a long, almost 100-year history.
    Krzysztof Stereńczak
  • Neural Rendering Our team's primary objective is to develop new representations for both NeRFs and Gaussian Splatting to address a fundamental challenge in neural rendering.
    Przemysław Spurek