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Matthew D. DiFranco
Matthew D. DiFranco, PhD

Center for Medical Physics and Biomedical Engineering
Position: Research Associate (Postdoc)

ORCID: 0000-0002-5844-1154
T +43 1 40400 19880


Data Interpretation, Statistical; Image Processing, Computer-Assisted; Microscopy; Nuclear Medicine; Osteoporosis; Prostate; Radiotherapy, Image-Guided

Research interests

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.


  • BONEMATCH - Bone Multi-modal Automated Trabecular Histomorphometry (fellow) (2012)
    Source of Funding: EU, FP7-PEOPLE-2010-IEF
    Principal Investigator

Selected publications

  1. 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:
  2. 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:
  3. 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:
  4. Nowosielski, M. and DiFranco, M.D., et al., 2014. An Intra-Individual Comparison of MRI, [18F]-FET and [18F]-FLT PET in Patients with High-Grade Gliomas A. Annala, ed. PLoS ONE, 9(4), p.e95830. Available at:
  5. DiFranco, M.D. et al., 2011. Ensemble based system for whole-slide prostate cancer probability mapping using color texture features. Computerized Medical Imaging and Graphics, 35(7-8), pp.629-645. Available at: