ScreenPoint has assembled a world-class team of researchers and engineers with extensive experience in breast imaging, machine learning, algorithm development, and clinical research. The team is highly motivated to develop technology that can make a difference in the early detection of breast cancer. Close collaboration with radiologists ensures that products meet clinical needs and fit in the workflow of a busy screening practice. Research is aimed at achieving superior performance of detection algorithms and on understanding the impact of our device Transpara™ in clinical practice.
R. Hupse, M. Samulski, M.B. Lobbes, R.M. Mann, R. Mus, G.J. den Heeten, D. Beijerinck, R.M. Pijnappel, C. Boetes and N. Karssemeijer. Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts. Radiology 2013;266:123-129.
R. Hupse, M. Samulski, M. Lobbes, A. den Heeten, M.W. Imhof-Tas, D. Beijerinck, R. Pijnappel, C. Boetes and N. Karssemeijer. Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses. European Radiology 2013;23:93-100.
M. Samulski, R. Hupse, C. Boetes, R. Mus, G. den Heeten and N. Karssemeijer. Using Computer Aided Detection in Mammography as a Decision Support. European Radiology 2010;20:2323-2330.
Funded by The European Regional Development Fund Netherlands/Germany, aims to assess and optimize breast cancer detection techniques in mammograms.
Funded by The European Regional Development Fund East Netherlands, aims to develop a clinically validated CAD system for mammography that is able to make optimal use of prior mammograms.
Funded by the Eurostars countries and by the European Union, aims to enhance detection algorithms in mammography and breast tomosynthesis by using the latest deep learning techniques.