Publications of Jonas Teuwen


  1. G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, W. Zhao, L. Yu, "Deep MRI reconstruction with radial subsampling", Medical Imaging 2022: Physics of Medical Imaging, 2022-4-4
  2. Y. Schirris, M. Engelaer, A. Panteli, H. M. Horlings, E. Gavves, J. Teuwen, J. E. Tomaszewski, A. D. Ward, "WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need", Medical Imaging 2022: Digital and Computational Pathology, 2022-4-4
  3. L. Balkenende, J. Teuwen, R. M. Mann, "Application of Deep Learning in Breast Cancer Imaging", Seminars in Nuclear Medicine, 2022-03-24
  4. K. A. Wahid, S. Ahmed, R. He, L. V. van Dijk, J. Teuwen, B. A. McDonald, V. Salama, A. S. R. Mohamed, T. Salzillo, C. Dede, N. Taku, S. Y. Lai, C. D. Fuller, M. A. Naser, "Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry", Clinical and Translational Radiation Oncology, January 1, 2022, 32
  5. R. Hou, L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, T. Lynch, M. van Oirsouw, K. Rogers, N. Stone, M. Wallis, J. Teuwen, J. Wesseling, E. S. Hwang, J. Y. Lo, "Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features", Radiology, 2022-01-04
  6. Y. Schirris, E. Gavves, I. Nederlof, H. M. Horlings, J. Teuwen, "DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer", Medical Image Analysis, 2022-07-01, 79


  1. Y. Beauferris, J. Teuwen, D. Karkalousos, N. Moriakov, M. Caan, G. Yiasemis, L. Rodrigues, A. Lopes, H. Pedrini, L. Rittner, M. Dannecker, V. Studenyak, F. Gröger, D. Vyas, S. Faghih-Roohi, A. K. Jethi, J. C. Raju, M. Sivaprakasam, M. Lasby, N. Nogovitsyn, W. Loos, R. Frayne, R. Souza, "Multi-Coil MRI Reconstruction Challenge – Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations", arXiv:2011.07952 [physics], 2021-12-21
  2. S. L. van Winkel, A. Rodríguez-Ruiz, L. Appelman, A. Gubern-Mérida, N. Karssemeijer, J. Teuwen, A. J. Wanders, I. Sechopoulos, R. M. Mann, "Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study", European Radiology, 2021
  3. G. Yiasemis, C. I. Sánchez, J. Sonke, J. Teuwen, "Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction", arXiv:2111.09639 [physics], 2021-11-18
  4. J. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen, I. Sechopoulos, "Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation", Medical Image Analysis, July 1, 2021, 71
  5. R. Sheombarsing, N. Moriakov, J. Sonke, J. Teuwen, "Subpixel object segmentation using wavelets and multi resolution analysis", arXiv:2110.15233 [cs, eess], 2021-10-28
  6. M. Garrett Fernandes, J. Bussink, B. Stam, R. Wijsman, D. A. X. Schinagl, R. Monshouwer, J. Teuwen, "Deep Learning Model for Automatic Contouring of Cardiovascular Substructures on Radiotherapy Planning CT Images: Dosimetric Validation and Reader Study based Clinical Acceptability Testing", Radiotherapy and Oncology, October 21, 2021
  7. A. Panteli, J. Teuwen, H. Horlings, E. Gavves, "Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations", Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2021, pp. 2813-2823
  8. W. B. G. Sanderink, J. Teuwen, L. Appelman, L. Moy, L. Heacock, E. Weiland, I. Sechopoulos, R. M. Mann, "Diffusion weighted imaging for evaluation of breast lesions: Comparison between high b-value single-shot and routine readout-segmented sequences at 3 T", Magnetic Resonance Imaging, December 1, 2021, 84
  9. P. M. Johnson, G. Jeong, K. Hammernik, J. Schlemper, C. Qin, J. Duan, D. Rueckert, J. Lee, N. Pezzotti, E. De Weerdt, S. Yousefi, M. S. Elmahdy, J. H. F. Van Gemert, C. Schülke, M. Doneva, T. Nielsen, S. Kastryulin, B. P. F. Lelieveldt, M. J. P. Van Osch, M. Staring, E. Z. Chen, P. Wang, X. Chen, T. Chen, V. M. Patel, S. Sun, H. Shin, Y. Jun, T. Eo, S. Kim, T. Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, N. Miriakov, B. Bakker, M. Caan, M. Welling, M. J. Muckley, F. Knoll, N. Haq, P. Johnson, A. Maier, T. Würfl, J. Yoo, "Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge", Machine Learning for Medical Image Reconstruction, 2021
  10. M. J. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha, S. Arberet, D. Nickel, Z. Ramzi, P. Ciuciu, J. Starck, J. Teuwen, D. Karkalousos, C. Zhang, A. Sriram, Z. Huang, N. Yakubova, Y. Lui, F. Knoll, "Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction", IEEE Transactions on Medical Imaging, 2021
  11. G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, "Deep MRI Reconstruction with Radial Subsampling", arXiv:2108.07619 [physics], 2021-08-20
  12. W. B. Sanderink, J. Teuwen, L. Appelman, L. Moy, L. Heacock, E. Weiland, N. Karssemeijer, P. A. Baltzer, I. Sechopoulos, R. M. Mann, "Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI", European Journal of Radiology, 2021, 138
  13. M. Caballo, A. M. Hernandez, S. H. Lyu, J. Teuwen, R. M. Mann, B. van Ginneken, J. M. Boone, I. Sechopoulos, "Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features", Journal of Medical Imaging (Bellingham, Wash.), 2021-03, 8;(2):024501
  14. I. Sechopoulos, J. Teuwen, R. Mann, "Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art", Seminars in Cancer Biology, July 1, 2021, 72
  15. Y. Schirris, E. Gavves, I. Nederlof, H. M. Horlings, J. Teuwen, "DeepSMILE: Self-supervised heterogeneity-aware multiple instance learning for DNA damage response defect classification directly from H&E whole-slide images", arXiv:2107.09405 [cs, eess], 2021-07-20
  16. F. Ayatollahi, S. B. Shokouhi, R. M. Mann, J. Teuwen, "Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning", arXiv preprint arXiv:2102.03932, 2021


  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. K. Michielsen, N. Moriakov, J. Teuwen, I. Sechopoulos, "Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis", arXiv preprint arXiv:2009.01538, 2020
  8. J. Goudsmit, J. Teuwen, "Tussen data en theorie: Het venijn zit in de aard", Tijdschrift voor Toezicht, 2020
  9. N. Moriakov, J. Adler, J. Teuwen, "Kernel of CycleGAN as a Principle homogeneous space", arXiv preprint arXiv:2001.09061, 2020
  10. 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
  11. J. Teuwen, N. Moriakov, "Convolutional neural networks", Handbook of Medical Image Computing and Computer Assisted Intervention, 2020


  1. J. Teuwen, A. Hauptmann, M. Kachelriess, "Deep Learning for Image Reconstruction and Processing", MEDICAL PHYSICS, 2019, 46
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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