W.X. (Mark) Huijgen MD

W.X. (Mark) Huijgen MD

PHD Candidate - OIO
  • Image Sciences Institute

Research Programs

Strategic Program Cancer



Mark is PhD candidate in the group of dr. Kenneth Gilhuijs, working on the LIMA project: Liquid Imaging and biopsies for improved (breast) cancer care. The aim of the project is to personalize cancer care by making better predictions on the effectivity of neoadjuvant chemotherapy.

His academic background is at Eindhoven University of Technology (TU/e). In 2015 he obtained his BSc degree with an award-winning thesis on the detection of early esophageal cancer, supervised by prof. Peter de With and dr. Fons van der Sommen. In 2018 he obtained an MSc degree in Biomedical Engineering and in Electrical Engineering (both cum laude), with a thesis on radiomic analysis of pancreatic cancer, for which he performed research at Radboudumc Nijmegen. For this research he was supervised prof. Josien Pluim, dr. Sveta Zinger and dr. ir. John Hermans.

Research Output (5)

Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

Suinesiaputra Avan, Mauger Charlène A., Ambale-Venkatesh Bharath, Bluemke David A., Dam Gade Josefine, Gilbert Kathleen, Janse Markus, Hald Line Sofie, Werkhoven Conrad, Wu Colin O., Lima Joao A. C., Young Alistair A. 21 Jan 2022, In: Frontiers in cardiovascular medicine. 8 , p. 807728

Volumetric breast density estimation on MRI using explainable deep learning regression

van der Velden Bas H M, Janse Markus H A, Ragusi Max A A, Loo Claudette E, Gilhuijs Kenneth G A 22 Oct 2020, In: Scientific Reports. 10

Interpretable deep learning regression for breast density estimation on MRI

Van Der Velden Bas H.M., Ragusi Max A.A., Janse Markus H.A., Loo Claudette E., Gilhuijs Kenneth G.A. 1 Jan 2020,

Quantitative CT-based radiomics of pancreatic ductal adenocarcinoma: a valuable tool for radiological staging?

Janse Mark, Litjens Geke, Zinger Svitlana, De With Peter H.N., Prokop Mathias, Hermans J. 4 Jun 2019, 10

Early esophageal cancer detection using RF classifiers

Janse Mark, Van Der Sommen Fons, Zinger Svitlana, Schoon Erik J., De With Peter H.N. 1 Jan 2016,

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