• Feilke, M., Bischl, B., Schmid, V.J., Gertheiss, J.: Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data. Methods of Information in Medicine 55:1 (2016) 31–41. [DOI]
  • Meyer-Baese, A., Schmid, V.J.: Pattern Recognition and Signal Analysis in Medical Imaging. Academic Press (2014), 2nd edition. ISBN: 978-0-12-409545-8. [Link]
  • Sommer, J., Schmid, V.J.: Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging. Journal of the Royal Statistical Society, Series C – Applied Statistics 63:5 (2014) 695-713. [DOI]
  • Sommer, J., Gertheiss, J., Schmid, V.J.: Spatially regularized estimation for the analysis of DCE-MRI data. Statistics in Medicine 33:6 (2014) 1029-41. [DOI]
  • Schmidt, P., Schmid, V.J., Gaser, C., Buck, D., Bührlen, S., Föschler, A., Mühlau, M.: Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities. PLOS One 8:7 (2013) e68196. [DOI]
  • Copley, S.J., Giannarou, S., Schmid, V.J., Hansell, D.M., Wells, A.U., Yang, G.-Z.: Effect of Ageing on Lung Microstructure in vivo: Assessment with Densitometric and Textural Analysis of High resolution CT Data. Journal of Thoracic Imaging 27:6 (2012) 366-371. [DOI]
  • Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler A., Berthele, A. Hoshi, M., Ilg, R., Schmid, V.J., Zimmer, C., Hemmer, B., Mühlau, M.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 59:4 (2012) 3774-3783. [DOI]
  • Mohajer, M., Schmid, V.J., Engels, N.A., Noel, P.B., Rummeney, E., Englmeier, K.H.: Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets. In: SPIE Medical Imaging (2012) 8317 [DOI]
  • K. Tabelow, J.D. Clayden, P. Lafaye de Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher: Image Analysis and Statistical Inference in Neuroimaging with R. NeuroImaging 55:4 (2011) 1686-1693. [DOI]
  • Mohajer, M., Schmid, V.J., Braren, R., Noel, P.B., Englmeier, K.H.: How Heterogeneous is the Liver? A Cluster Analyse of DCE-MRI Time Series. NSS-MIC 2011,p. 2483-2487 [DOI]
  • Whitcher, B., Schmid, V.J.: Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R. Journal of Statistical Software 44:5 (2011). [Link]
  • Whitcher, B., Schmid, V.J., Thornton, A.: Working with the DICOM and NIfTI Data Standards in R. Journal of Statistical Software 44:6 (2011). [Link]
  • Schmid, V.J.: Voxel based adaptive spatio-temporal modelling of perfusion cardiovascular MRI. IEEE Transactions on Medical Imaging 30:7 (2011) 1305-1313.[DOI]
  • Whitcher, B., Schmid, V.J., Collins, D.J., Orton, M.R., Koh, D.-M., Diaz de Corcuera, I., Parera, M., del Campo, J.M., deSouza, N.M., Leach, M., Harrington, K., El-Hariry, I.A.: A Bayesian Hierarchical Model for Dynamic Contrast-Enhanced MRI: A Phase II Study in Advanced Squamous Cell Carcinoma of the Head and Neck. Magnetic Resonance Materials in Physics, Biology and Medicine 24:2 (2011) 85-96. [DOI]
  • Schmid, V.J.: Kinetic Models for Cancer Imaging. In: Hamid R. Arabnia: Advances in Computational Biology. Heidelberg: Springer (2010). ISBN 978-1-4419-5912-6 [DOI] – Erratum [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z.: Quantitative analysis of Dynamic contrast-enhanced MR images based on Bayesian P-Splines. IEEE Transactions on Medical Imaging 28 (2009) 789-798 [DOI]
  • Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.: A Bayesian Hierarchical Model for the Analysis of a Longitudinal Dynamic Contrast-Enhanced MRI Cancer Study. Magnetic Resonance in Medicine 61 (2009) 163-174 [DOI]
  • Schmid, V.J., Gatehouse, P.D. Yang, G.Z.: Attenuation resilient AIF estimation based on hierarchical Bayesian modelling for first pass myocardial perfusion MRI. In: N.Ayache, S.Ourselin, A.Maeder (Eds.).: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, Part I, LNCS 4791, Berlin: Springer (2007) 393-400 [DOI]
  • Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.: Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging. IEEE Transactions on Medical Imaging 25 (2006) 1627-1636 [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z.: Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines. In Larsen, R., Nielsen, M., Sporring, J., eds.: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. Number 4190 in Lecture Notes in Computer Science, Berlin: Springer (2006) 679–686 [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z., Taylor, N.J., Padhani, A.R.: Statistical analysis of pharmacokinetical models in dynamic contrast-enhanced magnetic resonance imaging. In Duncan, J., Gerig, G., eds.: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. Number 3750 in Lecture Notes in Computer Science, Berlin: Springer (2005) 886–893 [DOI]
  • Kärcher, J., Schmid, V.J.: Two tissue compartment model in DCE-MRI: A Bayesian Approach. In: IEEE International Symposium on Biomedical Imaging. From Nano to Macro (2010) 724-727 [DOI]