Publications of Jonas Teuwen

Papers in international journals

1. C. Jahangir, D. Page, G. Broeckx, C. Gonzalez, C. Burke, C. Murphy, J. Reis-Filho, A. Ly, P. Harms, R. Gupta, M. Vieth, A. Hida, M. Kahila, Z. Kos, P. van Diest, S. Verbandt, J. Thagaard, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, S. Adams, J. Almeida, I. Alvarado-Cabrero, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, O. Burgues, A. Chardas, M. Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, F. Dantas Portela, F. Deman, S. Demaria, S. Dudgeon, M. Elghazawy, C. Fernandez-Martín, S. Fineberg, S. Fox, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, S. Hart, J. Hartman, S. Hewitt, H. Horlings, Z. Husain, S. Irshad, E. Janssen, T. Kataoka, K. Kawaguchi, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, G. Akturk, E. Scott, A. Kovács, A. L\aenkholm , C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, D. Kharidehal, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, N. Rajpoot, B. Rapoport, T. Rau, J. Ribeiro, D. Rimm, A. Vincent-Salomon, J. Saltz, S. Sayed, E. Hytopoulos, S. Mahon, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, G. Verghese, G. Viale, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, E. Specht Stovgaard, R. Salgado, W. Gallagher and A. Rahman, "Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2024;262:271-288.
   Abstract DOI PMID
2. J. Thagaard, G. Broeckx, D. Page, C. Jahangir, S. Verbandt, Z. Kos, R. Gupta, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, M. Amgad, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Balslev, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, A. Chardas, M. U Chon Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, A. Dahl, F. Dantas Portela, F. Deman, S. Demaria, J. Doré Hansen, S. Dudgeon, T. Ebstrup, M. Elghazawy, C. Fernandez-Martín, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, S. Hart, J. Hartman, S. Hauberg, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Roslind, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, E. Scott, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, S. Fineberg, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, R. Zin, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2023;260:498-513.
   Abstract DOI Cited by ~6
3. D. Page, G. Broeckx, C. Jahangir, S. Verbandt, R. Gupta, J. Thagaard, R. Khiroya, Z. Kos, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, M. Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, F. Dantas Portela, F. Deman, S. Demaria, S. Dudgeon, M. Elghazawy, S. Ely, C. Fernandez-Martín, S. Fineberg, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, A. Hardas, S. Hart, J. Hartman, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2023;260:514-532.
   Abstract DOI Cited by ~14
4. R. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen and R. Mann, "Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI", Diagnostics, 2022;12:1690.
   Abstract DOI PMID
5. 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 and R. Souza, "Multi-Coil MRI Reconstruction Challenge--Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations", Frontiers in Neuroscience, 2022;16.
   Abstract DOI PMID Cited by ~16
6. S. van Winkel, A. Rodríguez-Ruiz, L. Appelman, A. Gubern-Mérida, N. Karssemeijer, J. Teuwen, A. Wanders, I. Sechopoulos and R. 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;31:8682-8691.
   Abstract DOI PMID Cited by ~35
7. J. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen and I. Sechopoulos, "Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation", Medical Image Analysis, 2021;71:102061.
   Abstract DOI PMID Cited by ~23
8. M. Caballo, A. Hernandez, S. Lyu, J. Teuwen, R. Mann, B. van Ginneken, J. Boone and 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, 2021;8.
   Abstract DOI PMID Cited by ~5
9. W. Sanderink, J. Teuwen, L. Appelman, L. Moy, L. Heacock, E. Weiland, N. Karssemeijer, P. Baltzer, I. Sechopoulos and R. 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;138:109626.
   Abstract DOI PMID Cited by ~8
10. N. Lessmann, C. Sánchez, L. Beenen, L. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. van Everdingen, P. Gerke, B. Geurts, H. Gietema, M. Groeneveld, L. van Harten, N. Hendrix, W. Hendrix, H. Huisman, I. Isgum, C. Jacobs, R. Kluge, M. Kok, J. Krdzalic, B. Lassen-Schmidt, K. van Leeuwen, J. Meakin, M. Overkamp, T. van Rees Vellinga, E. van Rikxoort, R. Samperna, C. Schaefer-Prokop, S. Schalekamp, E. Scholten, C. Sital, L. Stöger, J. Teuwen, K. Vaidhya Venkadesh, C. de Vente, M. Vermaat, W. Xie, B. de Wilde, M. Prokop and B. van Ginneken, "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.
    Abstract DOI PMID Algorithm Download Cited by ~112
11. T. Kootstra, J. Teuwen, J. Goudsmit, T. Nijboer, M. Dodd and 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.
    Abstract DOI PMID Cited by ~7
12. I. Sechopoulos, J. Teuwen and R. Mann, "Artificial Intelligence for Breast Cancer Detection in Mammography: state of the art", Seminars in Cancer Biology, 2020.
    Abstract DOI PMID Cited by ~117
13. I. Olaciregui-Ruiz, I. Torres-Xirau, J. Teuwen, U. van der Heide and A. Mans, "A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system", Physica Medica, 2020;71:124-131.
    Abstract DOI PMID Cited by ~11
14. F. Ayatollahi, S. Shokouhi and 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.
    Abstract DOI PMID Cited by ~6
15. J. Goudsmit and J. Teuwen, "Tussen data en theorie", Tijdschrift voor Toezicht, 2020;11(1):48-53.
    Abstract DOI
16. G. Litjens, F. Ciompi, J. Wolterink, B. de Vos, T. Leiner, J. Teuwen and I. Isgum, "State-of-the-Art Deep Learning in Cardiovascular Image Analysis", JACC Cardiovascular Imaging, 2019;12(8 Pt 1):1549-1565.
    Abstract DOI PMID Download Cited by ~246
17. A. Rodriguez-Ruiz, K. Lang, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, M. Chevalier, T. Mertelmeier, M. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos and R. Mann, "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.
    Abstract DOI PMID Cited by ~137
18. M. Dalmis, A. Gubern-Mérida, S. Vreemann, P. Bult, N. Karssemeijer, R. Mann and J. Teuwen, "Artificial Intelligence Based Classification of Breast Lesions Imaged With a Multi-Parametric Breast MRI Protocol With ultrafast DCE-MRI, T2 and DWI", Investigative Radiology, 2019;56(6):325-332.
    Abstract DOI PMID Cited by ~74
19. S. van de Leemput, J. Teuwen, B. van Ginneken and R. Manniesing, "MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks", Journal of Open Source Software, 2019;4(39):1576.
    Abstract DOI Code Cited by ~13
20. A. Rodriguez-Ruiz, J. Teuwen, S. Vreemann, R. Bouwman, R. van Engen, N. Karssemeijer, R. Mann, A. Gubern-Merida and I. Sechopoulos, "New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers", Acta Radiologica, 2018;59(9):1051-1059.
    Abstract DOI PMID Cited by ~33
21. J. Teuwen, "On the integral kernels of derivatives of the Ornstein-Uhlenbeck semigroup", Infinite Dimensional Analysis, Quantum Probability and Related Topics, 2016;19(04):1650030.
    Abstract DOI
22. J. Teuwen, "A note on Gaussian maximal function", Indagationes Mathematicae, 2015;26(1):106-112.
    Abstract DOI

Preprints

1. E. Sogancioglu, B. van Ginneken, F. Behrendt, M. Bengs, A. Schlaefer, M. Radu, D. Xu, K. Sheng, F. Scalzo, E. Marcus, S. Papa, J. Teuwen, E. Scholten, S. Schalekamp, N. Hendrix, C. Jacobs, W. Hendrix, C. Sánchez and K. Murphy, "Nodule detection and generation on chest X-rays: NODE21 Challenge", arXiv:2401.02192, 2024.
   Abstract DOI PMID arXiv
2. M. 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 and F. Knoll, "Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction", arXiv:2012.06318, 2020.
   Abstract DOI arXiv Cited by ~43
3. 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. Jethi, J. Raju, M. Sivaprakasam, M. Lasby, N. Nogovitsyn, W. Loos, R. Frayne and R. Souza, "Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations", arXiv:2011.07952, 2020.
   Abstract DOI arXiv Cited by ~7
4. P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan and M. Welling, "i-RIM applied to the fastMRI challenge", arXiv:1910.08952, 2019.
   Abstract arXiv Cited by ~29
5. R. Dilz, L. Schröder, N. Moriakov, J. Sonke and J. Teuwen, "Learned SIRT for Cone Beam Computed Tomography Reconstruction", arXiv:1908.10715, 2019.
   Abstract arXiv Cited by ~1
6. T. de Moor, A. Rodriguez-Ruiz, R. Mann and J. Teuwen, "Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network", arXiv:1802.06865, 2018.
   Abstract arXiv
7. J. Teuwen and P. Urbach, "On Maximum Focused Electric Energy in Bounded Regions", arXiv:1801.02450, 2018.
   Abstract arXiv

Papers in conference proceedings

1. N. Moriakov, J. Adler and J. Teuwen, "Kernel of CycleGAN as a principal homogeneous space", International Conference on Learning Representations, 2020.
   Abstract Url Cited by ~10
2. K. Michielsen, N. Moriakov, J. Teuwen and I. Sechopoulos, "Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis", 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.
   Abstract Cited by ~1
3. M. Caballo, J. Teuwen, R. Mann and I. Sechopolous, "Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images", Medical Imaging, 2019.
   Abstract DOI Cited by ~5
4. J. van Vugt, E. Marchiori, R. Mann, A. Gubern-Merida, N. Moriakov and J. Teuwen, "Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation", Medical Imaging, 2019.
   Abstract DOI
5. D. Ruhe, V. Codreanu, C. van Leeuwen, D. Podareanu, V. Saletore and J. Teuwen, "Generating CT-scans with 3D Generative Adversarial Networks Using a Supercomputer", Medical Imaging meets NeurIPS, 2019.
   Abstract
6. N. Moriakov, K. Michielsen, R. Mann, J. Adler, I. Sechopolous and J. Teuwen, "Deep learning framework for digital breast tomosynthesis reconstruction", Medical Imaging, 2019.
   Abstract DOI arXiv Cited by ~10
7. Y. Hagos, A. Gubern-Mérida and J. Teuwen, "Improving Breast Cancer Detection using Symmetry Information with Deep Learning", Breast Image Analysis (BIA), 2018.
   Abstract DOI Cited by ~25
8. S. van de Leemput, J. Teuwen and R. Manniesing, "MemCNN: a Framework for Developing Memory Efficient Deep Invertible Networks", International Conference on Learning Representations, 2018.
   Abstract Url Cited by ~11
9. T. de Moor, A. Rodriguez-Ruiz, R. Mann and J. Teuwen, "Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network", International Workshop on Breast Imaging, 2018.
   Abstract arXiv Cited by ~32
10. A. Rodriguez-Ruiz, J. Teuwen, K. Chung, N. Karssemeijer, M. Chevalier, A. Gubern-Merida and I. Sechopoulos, "Pectoral muscle segmentation in breast tomosynthesis with deep learning", Medical Imaging, 2018.
    Abstract DOI Cited by ~21
11. M. Ghafoorian, J. Teuwen, R. Manniesing, F. de Leeuw, B. van Ginneken, N. Karssemeijer and B. Platel, "Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR", Medical Imaging, 2018;10574:105742U.
    Abstract DOI arXiv Cited by ~18

Abstracts

1. W. Sanderink, J. Teuwen, L. Appelman, I. Sechopoulos, N. Karssemeijer and R. Mann, "Simultaneous multi-slice single-shot DWI compared to routine read-out-segmented DWI for evaluation of breast lesions", ISMRM Benelux, 2020.
   Abstract
2. W. Sanderink, J. Teuwen, L. Appelman, I. Sechopoulos, N. Karssemeijer and R. Mann, "Simultaneous multi-slice single-shot DWI compared to routine read-out-segmented DWI for evaluation of breast lesions", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2019.
   Abstract
3. E. Smeets, J. Teuwen, J. van der Laak, M. Gotthardt, F. Ciompi and E. Aarntzen, "Tumor heterogeneity as a PET-biomarker predicts overall survival of pancreatic cancer patients", European Society for Molecular Imaging, 2018.
   Abstract
4. J. Teuwen, M. Kallenberg, A. Gubern-Merida, A. Rodriguez-Ruiz, N. Karssemeijer and R. Mann, "Automated pre-selection of mammograms without abnormalities using deep learning", Annual Meeting of the Radiological Society of North America, 2017.
   Abstract

PhD theses

1. M. Dalmis, "Automated Analysis of Breast MRI From traditional methods into deep learning", PhD thesis, 2019.
   Abstract Url

Other publications

1. C. Rao, S. Pai, I. Hadzic, I. Zhovannik, D. Bontempi, A. Dekker, J. Teuwen and A. Traverso, "Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge", Head and Neck Tumor Segmentation, 2021:65-77.
   Abstract DOI Cited by ~3