Publications

2022

  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
  7. 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. Kumar Jethi, J. Chandra 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", Frontiers in Neuroscience, 2022-07-06, 16
  8. G. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, "DIRECT: Deep Image REConstruction Toolkit", Journal of Open Source Software, 2022-05-30, 7;(73):4278

2021

  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. X. Liu, S. Pintea, F. K. Nejadasl, O. Booij, J. van Gemert, "No Frame Left Behind: Full Video Action Recognition", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021
  3. 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
  4. Y. Lin, S. Pintea, J. van Gemert, "Semi-Supervised Lane Detection With Deep Hough Transform", 2021 IEEE International Conference on Image Processing (ICIP), 2021-09
  5. S. Pintea, N. Tomen, S. Goes, M. Loog, J. van Gemert, "Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory", IEEE Transactions on Image Processing, 2021, 30
  6. N. Tomen, S. Pintea, J. van Gemert, "Deep Continuous Networks", International Conference on Machine Learning, 2021/07/01
  7. 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
  8. 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
  9. U. Gürsoy, D. Kharzeev, E. Marcus, K. Rajagopal, C. Shen, "Charge-dependent flow induced by electromagnetic fields in heavy ion collisions", Nuclear Physics A, January 1, 2021, 1005
  10. C. Couzens, E. Marcus, K. Stemerdink, D. van de Heisteeg, "The near-horizon geometry of supersymmetric rotating AdS4 black holes in M-theory", Journal of High Energy Physics, 2021-05-21, 2021;(5):194
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. J. B. van den Berg, R. Sheombarsing, "Rigorous numerics for ODEs using Chebyshev series and domain decomposition", Journal of Computational Dynamics, 2021, 8;(3):353
  18. J. Minnema, M. van Eijnatten, H. der Sarkissian, S. Doyle, J. Koivisto, J. Wolff, T. Forouzanfar, F. Lucka, K. J. Batenburg, "Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction", Physics in Medicine & Biology, 2021-07, 66;(13):135015
  19. G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, "Deep MRI Reconstruction with Radial Subsampling", arXiv:2108.07619 [physics], 2021-08-20
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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 COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence", Radiology, 2021, 298;(1):E18-E28

2020

  1. I. Lelekas, N. Tomen, S. Pintea, J. van Gemert, "Top-Down Networks: A Coarse-to-Fine Reimagination of CNNs", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020
  2. Y. Lin, S. Pintea, J. van Gemert, A. Vedaldi, H. Bischof, T. Brox, J. Frahm, "Deep Hough-Transform Line Priors", Computer Vision – ECCV 2020, 2020
  3. C. Hull, E. Marcus, K. Stemerdink, S. Vandoren, "Black holes in string theory with duality twists", Journal of High Energy Physics, 2020-07-14, 2020;(7):86
  4. J. B. van den Berg, R. Sheombarsing, "Validated computations for connecting orbits in polynomial vector fields", Indagationes Mathematicae, 03/2020, 31;(2):310-373
  5. N. Moriakov, "Computable Følner monotilings and a theorem of Brudno", Ergodic Theory and Dynamical Systems, 2020
  6. A. Panteli, D. K. Gupta, N. Bruijn, E. Gavves, "Siamese Tracking of Cell Behaviour Patterns", Medical Imaging with Deep Learning, 2020-09-21
  7. 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