Matthew D. Difranco
Research Associate (Postdoc)
Center for Medical Physics and Biomedical Engineering
Medical University of Vienna
Austria
Biography
Dr. Matthew D. DiFranco is currently working as a Research Associate (Postdoc) in the Department of Center for Medical Physics and Biomedical Engineering, Medical University of Vienna , Austria. His research interests includes Medical Physics and Biomedical Engineering. He /she is serving as an editorial member and reviewer of several international reputed journals. Dr. Matthew D. DiFranco is the member of many international affiliations. He/ She has successfully completed his Administrative responsibilities. He /she has authored of many research articles/books related to Medical Physics and Biomedical Engineering.
Research Interest
Matthew DiFranco earned his PhD in Computer Science from University College Dublin in 2010. His doctoral research focused on machine learning in whole-slide digital pathology. Following his doctoral studies, Matthew worked as a post-doc at Innsbruck Medical University 4D Visualization Lab on project Rhinospider, an Austrian Wirtschaftsservice (aws) funded project for the research and development of a novel surgical navigation device. Matthew received a Marie Curie Intra-European Fellowship (IEF) in 2012 to carry out research in multi-modal bone imaging at the Computational Imaging Research (CIR) , Department of Biomedical Imaging and Image-guided Therapy. He subsequently joined the Center for Medical Physics and Biomedical Engineering in 2014 to continue research on computational methods in multi-modal biomedical imaging, including PET/MR, PET/CT, and digital pathology.
Publications
-
Valentinitsch, A., et al., DiFranco, M.D., et al., 2015. Trabecular bone class mapping across resolutions: translating methods from HR-pQCT to clinical CT S. Ourselin & M. A. Styner, eds. Medical Imaging 2015: Image Processing. Available at: http://dx.doi.org/10.1117/12.2081187.
-
DiFranco, M.D. et al., 2015. Performance assessment of automated tissue characterization for prostate H and E stained histopathology M. N. Gurcan & A. Madabhushi, eds. Medical Imaging 2015: Digital Pathology. Available at: http://dx.doi.org/10.1117/12.2081787.
-
Weingant, M., et al., DiFranco, M.D., 2015. Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation. Lecture Notes in Computer Science, pp.280-287. Available at: http://dx.doi.org/10.1007/978-3-319-24888-2_34.
