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.
Key publications on research forming the basis for our technology are listed below.
Image Analysis and Machine Learning
T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Mérida, C.I. Sánchez, R. Mann, A. den Heeten and N. Karssemeijer. Large Scale Deep Learning for Computer Aided Detection of Mammographic Lesions. Medical Image Analysis
J.J. Mordang, T. Janssen, A. Bria, T. Kooi, A. Gubern-Mérida and N. Karssemeijer. Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks. in: Breast Imaging, volume 9699 of Lecture Notes in Computer Science, 2016, pages 35-42.
A. Bria, N. Karssemeijer and F. Tortorella. Learning from unbalanced data: A cascade-based approach for detecting clustered microcalcifications. Medical Image Analysis 2013;18:241-252.
R. Hupse and N. Karssemeijer. Use of normal tissue context in computer-aided detection of masses in mammograms. IEEE Transactions on Medical Imaging 2009;28:2033-2041.
N. Karssemeijer, G.M. Te Brake. Detection of stellate distortions in mammograms. IEEE Transactions on Medical Imaging 1996;15(5):611-9.
N. Karssemeijer. Automated classification of parenchymal patterns in mammograms. Physics in Medicine and Biology 1998;43(2):365-78.
Transpara™ in Clinical Practice
A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, m. Chevalier, T. Tan, T. Mertelmeier, W. Wallis, I. Andersson, S. Zackrisson, R. Mann, I. Sechopoulos. Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists. Journal of the National Cancer Institute 2018, In press.
A. Rodriguez-Ruiz, E. Krupinski, J. Mordang, K. Schilling, S. Heywang-Kobrunner, I. Sechopoulos, R. Mann. Detection of breast cancer using mammography: Impact of an Artificial Intelligence support system. Radiology 2018, In press.
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.
ScreenPoint Participates in the Following Funded Research Projects
InMediValue. Funded by The European Regional Development Fund Netherlands/Germany, aims to assess and optimize breast cancer detection techniques in mammograms.
MARBLE. 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.
IBSCREEN. 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.