M.H.A. (Mark) Janse MSc

M.H.A. (Mark) Janse MSc

PHD Candidate - OIO
  • Image Sciences Institute

Research Programs




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 (8)

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head

Litjens Geke, Broekmans Joris P.E.A., Boers Tim, Caballo Marco, van den Hurk Maud H.F., Ozdemir Dilek, van Schaik Caroline J., Janse Markus H.A., van Geenen Erwin J.M., van Laarhoven Cees J.H.M., Prokop Mathias, de With Peter H.N., van der Sommen Fons, Hermans John J. Oct 2023, In: Diagnostics. 13 14 p.

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden:A Multiinstitutional Cohort Study

Janse Markus H.A., Janssen Liselore M., van der Velden Bas H.M., Moman Maaike R., Wolters-van der Ben Elian J.M., Kock Marc C.J.M., Viergever Max A., van Diest Paul J., Gilhuijs Kenneth G.A. 17 Mar 2023, In: Journal of Magnetic Resonance Imaging. 58 , p. 1739-1749 11 p.

Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI:Protocol of the LIMA study-a multicentre prospective observational cohort study

Janssen Liselore M., Suelmann Britt B.M., Elias Sjoerd G., Janse Markus H.A., Van Diest Paul J., Van Der Wall Elsken, Gilhuijs Kenneth G.A. 20 Sep 2022, In: BMJ Open. 12 , p. 1-8

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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