1. N. Moriakov, A. Samudre, M. Negro, F. Gieseke, S. Otten, L. Hendriks, "Inferring astrophysical X-ray polarization with deep learning", arXiv:2005.08126 [astro-ph], 2020-05-16
  2. T. Driever, A. Van der Horst, J. Teuwen, M. Fast, J. Sonke, "Quantifying intra-fractional gastric wall motion for MR-guided radiotherapy", ESTRO 2020, 2020
  3. M. Fernandes, J. Teuwen, R. Wijsman, S. Barbara, N. Moriakov, J. Bussink, R. Monshouwer, "Segmentation of the heart using a Residual U-net model", ESTOR 2020, 2020
  4. C. Linthorst, R. Wijsman, M. Fernandes, B. Stam, J. Teuwen, D. Bosboom, R. Monshouwer, J. Bussink, "Pericardial effusion after radiotherapy for Non-Small Cell Lung Cancer", ESTRO 2020, 2020
  5. A. Tiwari, A. Pai, D. Joshi, "A shoe-mounted infrared sensor-based instrumentation for locomotion identification using machine learning methods", Measurement, 10/09/2020, 168;(108458)
  6. C. Rao, S. Pai, I. Hadzic, I. Zhovannik, D. Bontempi, A. Dekker, J. Teuwen, A. Traverso, "Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge", Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings, 2020
  7. I. Olaciregui-Ruiz, I. Torres-Xirau, J. Teuwen, U. A. van der Heide, A. Mans, "A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system", Physica Medica, 03/2020, 71
  8. T. Kootstra, J. Teuwen, J. Goudsmit, T. Nijboer, M. Dodd, S. Van der Stigchel, "Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features", Journal of Vision, 2020, 20;(9):1-1
  9. K. Michielsen, N. Moriakov, J. Teuwen, I. Sechopoulos, "Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis", arXiv preprint arXiv:2009.01538, 2020
  10. J. Goudsmit, J. Teuwen, "Tussen data en theorie: Het venijn zit in de aard", Tijdschrift voor Toezicht, 2020
  11. N. Moriakov, J. Adler, J. Teuwen, "Kernel of CycleGAN as a Principle homogeneous space", arXiv preprint arXiv:2001.09061, 2020
  12. N. Lessmann, C. I. Sánchez, L. Beenen, L. H. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. M. van Everdingen, P. K. Gerke, "Automated assessment of CO-RADS and chest CT severity scores in patients with suspected COVID-19 using artificial intelligence", Radiology, 2020
  13. F. Ayatollahi, S. B. Shokouhi, J. Teuwen, "Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features", International journal of computer assisted radiology and surgery, 2020, 15;(2):297-307
  14. J. Teuwen, N. Moriakov, "Convolutional neural networks", Handbook of Medical Image Computing and Computer Assisted Intervention, 2020


  1. E. Marcus, S. Vandoren, "A new class of SYK-like models with maximal chaos", Journal of High Energy Physics, 2019-01-22, 2019;(1):166
  2. J. Teuwen, A. Hauptmann, M. Kachelriess, "Deep Learning for Image Reconstruction and Processing", MEDICAL PHYSICS, 2019, 46
  3. M. U. Dalmis, A. Gubern-Mérida, S. Vreemann, P. Bult, N. Karssemeijer, R. Mann, J. Teuwen, "Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI", Investigative Radiology, June 2019, 54;(6):325–332
  4. G. Litjens, F. Ciompi, J. M. Wolterink, B. D. de Vos, T. Leiner, J. Teuwen, I. Išgum, "State-of-the-art deep learning in cardiovascular image analysis", JACC: Cardiovascular Imaging, 2019, 12;(8 Part 1):1549-1565
  5. W. Sanderink, J. Teuwen, L. Appelman, I. Sechopoulos, R. Mann, "Simultaneous multi-slice single-shot DWI compared to routine read-out-segmented DWI for evaluation of breast lesions", ISMRM, 2019
  6. A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. H. Helbich, M. Chevalier, T. Mertelmeier, "Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study", European radiology, 2019, 29;(9):4825-4832
  7. P. Putzky, D. Karkalousos, J. Teuwen, N. Miriakov, B. Bakker, M. Caan, M. Welling, "i-RIM applied to the fastMRI challenge", arXiv preprint arXiv:1910.08952, 2019
  8. R. J. Dilz, L. Schröder, N. Moriakov, J. Sonke, J. Teuwen, "Learned SIRT for Cone Beam Computed Tomography Reconstruction", arXiv preprint arXiv:1908.10715, 2019
  9. M. Caballo, J. Teuwen, R. Mann, I. Sechopoulos, "Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images", Medical Imaging 2019: Computer-Aided Diagnosis, 2019, 10950
  10. J. van Vugt, E. Marchiori, R. Mann, A. Gubern-Mérida, N. Moriakov, J. Teuwen, "Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation", Medical Imaging 2019: Computer-Aided Diagnosis, 2019, 10950


  1. S. C. van de Leemput, J. Teuwen, R. Manniesing, "Memcnn: a framework for developing memory efficient deep invertible networks", ICLR 2018 Workshop Submission, 2018
  2. U. Gürsoy, D. Kharzeev, E. Marcus, K. Rajagopal, C. Shen, "Charge-dependent flow induced by magnetic and electric fields in heavy ion collisions", Physical Review C, November 15, 2018, 98;(5):055201
  3. R. S. Sheombarsing, "Validated Chebyshev-based Computations for Ordinary and Partial Differential Equations", PhD thesis, 2018
  4. N. Moriakov, "On Effective Birkhoff’s Ergodic Theorem for Computable Actions of Amenable Groups", Theory of Computing Systems, 7/2018, 62;(5):1269-1287
  5. E. Smeets, B. Feuerecker, J. Teuwen, J. van der Laak, M. Gotthardt, J. Siveke, R. Braren, F. Ciompi, E. Aarntzen, "Tumor heterogeneity as a PET-biomarker predicts overall survival of pancreatic cancer patients", European Society for Molecular Imaging 2018, 2018
  6. N. Antropova, A. L. Beam, B. K. Beaulieu-Jones, I. Chen, C. Chivers, A. Dalca, S. Finlayson, M. Fiterau, J. A. Fries, M. Ghassemi, "Machine learning for health (ML4H) workshop at NeurIPS 2018", arXiv preprint arXiv:1811.07216, 2018
  7. J. Teuwen, "[I253] Basics of deep learning", Physica Medica, 2018, 52
  8. A. Rodriguez-Ruiz, J. Teuwen, K. Chung, N. Karssemeijer, M. Chevalier, A. Gubern-Merida, I. Sechopoulos, "Pectoral muscle segmentation in breast tomosynthesis with deep learning", Medical Imaging 2018: Computer-Aided Diagnosis, 2018, 10575
  9. Y. B. Hagos, A. G. Mérida, J. Teuwen, "Improving breast cancer detection using symmetry information with deep learning", Image Analysis for Moving Organ, Breast, and Thoracic Images, 2018
  10. T. de Moor, A. Rodriguez-Ruiz, A. G. Mérida, R. Mann, J. Teuwen, "Automated lesion detection and segmentation in digital mammography using a u-net deep learning network", 14th International Workshop on Breast Imaging (IWBI 2018), 2018, 10718
  11. M. Ghafoorian, J. Teuwen, R. Manniesing, F. de Leeuw, B. van Ginneken, N. Karssemeijer, B. Platel, "Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain MR", Medical Imaging 2018: Image Processing, 2018, 10574
  12. J. Teuwen, P. Urbach, "On Maximum Focused Electric Energy in Bounded Regions", arXiv preprint arXiv:1801.02450, 2018
  13. A. Rodriguez-Ruiz, J. Teuwen, S. Vreemann, R. W. Bouwman, R. E. van Engen, N. Karssemeijer, R. M. Mann, A. Gubern-Merida, I. Sechopoulos, "New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers", Acta Radiologica, 2018, 59;(9):1051-1059


  1. S. Otto, K. Nitsche, C. Jung, A. Kryvanos, A. Zhylka, K. Heitkamp, J. Gutiérrez-Chico, B. Goebel, P. C. Schulze, H. R. Figulla, T. C. Poerner, "Endothelial progenitor cells and plaque burden in stented coronary artery segments: an optical coherence tomography study six months after elective PCI", BMC Cardiovascular Disorders, 2017-04-26, 17;(1):103
  2. L. Gong, H. Wang, C. Peng, Y. Dai, M. Ding, Y. Sun, X. Yang, J. Zheng, "Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information", BioMedical Engineering OnLine, January 10, 2017, 16;(1):8
  3. A. Amenta, J. Teuwen, "$L^p$-$L^q$ off-diagonal estimates for the Ornstein–Uhlenbeck semigroup: some positive and negative results", Bulletin of the Australian Mathematical Society, 08/2017, 96;(1):154-161