Publications

Jonas Teuwen

2024

  1. Artificial Intelligence-Enhanced Breast MRI
    Investigative Radiology
    R. Lo Gullo, E. Marcus, J. Huayanay, S. Eskreis-Winkler, S. Thakur, J. Teuwen, K. Pinker, 2024
    Abstract
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  2. On retrospective k-space subsampling schemes for deep MRI reconstruction
    Magnetic Resonance Imaging
    G. Yiasemis, C. I. Sánchez, J. Sonke, J. Teuwen, 2024
    Abstract
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  3. 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
    C. A. Jahangir, D. B. Page, G. Broeckx, C. A. Gonzalez, C. Burke, C. Murphy, J. S. Reis‐Filho, A. Ly, P. W. Harms, R. R. Gupta, M. Vieth, A. I. Hida, M. Kahila, Z. Kos, P. J. Van Diest, S. Verbandt, J. Thagaard, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, S. Adams, J. S. Almeida, I. Alvarado‐Cabrero, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, O. Burgues, A. Chardas, M. C. U. Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, F. L. Dantas Portela, F. Deman, S. Demaria, S. N. Dudgeon, M. Elghazawy, C. Fernandez‐Martín, S. Fineberg, S. B. Fox, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, S. N. Hart, J. Hartman, S. Hewitt, H. M. Horlings, Z. Husain, S. Irshad, E. A. Janssen, T. R. Kataoka, K. Kawaguchi, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, G. Akturk, E. Scott, A. Kovács, A. Lænkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, D. Kharidehal, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. M. Ribeiro, D. Rimm, A. Vincent‐Salomon, J. Saltz, S. Sayed, E. Hytopoulos, S. Mahon, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, G. E. Verghese, G. Viale, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, E. Specht Stovgaard, R. Salgado, W. M. Gallagher, A. Rahman, 2024
    Abstract
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  4. To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
    L. Han, T. Tan, T. Zhang, Y. Gao, X. Wang, V. Longo, S. Ventura-Díaz, A. D'Angelo, J. Teuwen, R. Mann, 2024
    Abstract
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  5. Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
    N. Moriakov, J. Sonke, J. Teuwen, 2024
    Abstract
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  6. Deep Cardiac MRI Reconstruction with ADMM
    Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers
    G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2024
    Abstract
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  7. Artificial intelligence and explanation: How, why, and when to explain black boxes
    European Journal of Radiology
    E. Marcus, J. Teuwen, 2024
    Abstract
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  8. IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data
    Information Fusion
    T. Zhang, T. Tan, L. Han, X. Wang, Y. Gao, J. Van Dijk, A. Portaluri, A. Gonzalez-Huete, A. D’Angelo, C. Lu, J. Teuwen, R. Beets-Tan, Y. Sun, R. Mann, 2024, 108
    Abstract
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  9. Nodule detection and generation on chest X-rays: NODE21 Challenge
    IEEE Transactions on Medical Imaging
    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. T. Scholten, S. Schalekamp, N. Hendrix, C. Jacobs, W. Hendrix, C. I. Sánchez, K. Murphy, 2024
    Abstract
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  10. Optimizing CycleGAN design for CBCT-to-CT translation: insights into 2D vs 3D modeling, patch size, and the need for tailored evaluation metrics
    Medical Imaging 2024: Image Processing
    I. Hadzic, S. Pai, V. Trier Taasti, D. Bontempi, I. Zhovannik, R. Canters, J. J. Sonke, A. Dekker, J. Teuwen, A. Traverso, 2024
    Abstract
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  11. The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
    J. Lyu, C. Qin, S. Wang, F. Wang, Y. Li, Z. Wang, K. Guo, C. Ouyang, M. Tänzer, M. Liu, L. Sun, M. Sun, Q. Li, Z. Shi, S. Hua, H. Li, Z. Chen, Z. Zhang, B. Xin, D. N. Metaxas, G. Yiasemis, J. Teuwen, L. Zhang, W. Chen, Y. Pang, X. Liu, A. Razumov, D. V. Dylov, Q. Dou, K. Yan, Y. Xue, Y. Du, J. Dietlmeier, C. Garcia-Cabrera, Z. A. Hemidi, N. Vogt, Z. Xu, Y. Zhang, Y. Chu, W. Chen, W. Bai, X. Zhuang, J. Qin, L. Wu, G. Yang, X. Qu, H. Wang, C. Wang, 2024
    Abstract
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  12. Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.
    Cancer imaging: the official publication of the International Cancer Imaging Society
    S. Alaeikhanehshir, M. M. Voets, F. H. van Duijnhoven, E. H. Lips, E. J. Groen, M. C. J. van Oirsouw, S. E. Hwang, J. Y. Lo, J. Wesseling, R. M. Mann, J. Teuwen, 2024, 24;(1):48
    Abstract
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  13. AI Applications to Breast MRI: Today and Tomorrow.
    Journal of magnetic resonance imaging: JMRI
    R. Lo Gullo, J. Brunekreef, E. Marcus, L. K. Han, S. Eskreis-Winkler, S. B. Thakur, R. Mann, K. Groot Lipman, J. Teuwen, K. Pinker, 2024
    Abstract
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  14. Abstract 4951: Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology
    Cancer Research
    L. V. Leek, V. Botha, V. A. Baxi, J. Teuwen, H. M. Horlings, S. D. Chasalow, J. V. D. Haar, L. F. Wessels, A. Debroy, E. E. Voest, 2024, 84;(6_Supplement):4951-4951
    Abstract
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  15. End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
    G. Yiasemis, J. Sonke, J. Teuwen, 2024
    Abstract
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  16. Deep learning-based low-dose CT simulator for non-linear reconstruction methods.
    Medical physics
    S. A. M. Tunissen, N. Moriakov, M. Mikerov, E. J. Smit, I. Sechopoulos, J. Teuwen, 2024
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  17. Task-driven wavelets using constrained empirical risk minimization
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    E. Marcus, R. Sheombarsing, J. Sonke, J. Teuwen, 2024
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  18. Kandinsky conformal prediction: Efficient calibration of image segmentation algorithms
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    J. Brunekreef, E. Marcus, R. Sheombarsing, J. Sonke, J. Teuwen, 2024
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  19. Artificial intelligence
    Cancer Cell
    M. Gerstung, D. Liu, M. Ghassemi, J. Zou, D. Chowell, J. Teuwen, F. Mahmood, J. N. Kather, 2024, 42;(6):915-918
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2023

  1. STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data
    B. P. Y. Kwee, M. Messemaker, E. Marcus, G. Oliveira, W. Scheper, C. J. Wu, J. Teuwen, T. N. Schumacher, 2023
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  2. Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history
    X. Wang, T. Tan, Y. Gao, R. Su, T. Zhang, L. Han, J. Teuwen, A. D’Angelo, C. A. Drukker, M. K. Schmidt, R. Beets-Tan, N. Karssemeijer, R. Mann, 2023
    Abstract
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  3. Visualize what you learn: a well-explainable joint-learning framework based on multi-view mammograms and associated reports
    Y. Gao, H. Zhou, X. Wang, T. Zhang, R. Tan, L. Han, L. Estacio, A. D’Angelo, J. Teuwen, R. Mann, T. Tan, 2023
    Abstract
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  4. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease
    Cell Reports Medicine
    T. Zhang, T. Tan, X. Wang, Y. Gao, L. Han, L. Balkenende, A. D’Angelo, L. Bao, H. M. Horlings, J. Teuwen, R. G. Beets-Tan, R. M. Mann, 2023
    Abstract
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  5. 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
    D. B. Page, G. Broeckx, C. A. Jahangir, S. Verbandt, R. R. Gupta, J. Thagaard, R. Khiroya, Z. Kos, K. Abduljabbar, G. Acosta Haab, B. Acs, J. S. Almeida, I. Alvarado‐Cabrero, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, O. Burgues, M. C. U. Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, F. L. Dantas Portela, F. Deman, S. Demaria, S. N. Dudgeon, M. Elghazawy, S. Ely, C. Fernandez‐Martín, S. Fineberg, S. B. Fox, W. M. Gallagher, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, A. Hardas, S. N. Hart, J. Hartman, S. Hewitt, A. I. Hida, H. M. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. A. Janssen, M. Kahila, T. R. Kataoka, K. Kawaguchi, D. Kharidehal, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. S. Reis‐Filho, J. M. Ribeiro, D. Rimm, A. Salomon, M. Salto‐Tellez, J. Saltz, S. Sayed, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, P. J. Van Diest, G. E. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, S. Adams, J. M. S. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado, E. Specht Stovgaard, G. Akturk, N. Bouchmaa, 2023
    Abstract
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  6. Improving Lesion Volume Measurements on Digital Mammograms
    N. Moriakov, J. Peters, R. Mann, N. Karssemeijer, J. V. Dijck, M. Broeders, J. Teuwen, 2023
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  7. 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
    J. Thagaard, G. Broeckx, D. B. Page, C. A. Jahangir, S. Verbandt, Z. Kos, R. Gupta, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. S. Almeida, I. Alvarado‐Cabrero, M. Amgad, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. Balslev, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, N. Bouchmaa, O. Burgues, A. Chardas, M. Chon U Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, A. B. Dahl, F. L. Dantas Portela, F. Deman, S. Demaria, J. Doré Hansen, S. N. Dudgeon, T. Ebstrup, M. Elghazawy, C. Fernandez‐Martín, S. B. Fox, W. M. Gallagher, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, S. N. Hart, J. Hartman, S. Hauberg, S. Hewitt, A. I. Hida, H. M. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. A. Janssen, M. Kahila, T. R. Kataoka, K. Kawaguchi, D. Kharidehal, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. S. Reis‐Filho, J. M. Ribeiro, D. Rimm, A. Roslind, A. Vincent‐Salomon, M. Salto‐Tellez, J. Saltz, S. Sayed, E. Scott, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, S. Fineberg, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, P. J. Van Diest, G. E. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, R. M. Zin, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado, E. Specht Stovgaard, 2023, 260;(5):498-513
    Abstract
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  8. Development, validation, and simplification of a scanner‐specific CT simulator
    Medical Physics
    S. A. M. Tunissen, L. J. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. J. Smit, I. Sechopoulos, 2023
    Abstract
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  9. vSHARP: variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse-Problems
    G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2023
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  10. Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism
    Lecture Notes in Computer Science
    T. Zhang, L. Han, A. D’Angelo, X. Wang, Y. Gao, C. Lu, J. Teuwen, R. Beets-Tan, T. Tan, R. Mann, 2023
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  11. Beyond the AJR: A Breakthrough in the Use of Artificial Intelligence for Mammography in Screening for Breast Cancer
    American Journal of Roentgenology
    R. M. Mann, J. Teuwen, 2023
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  12. End‐to‐end memory‐efficient reconstruction for cone beam CT
    Medical Physics
    N. Moriakov, J. Sonke, J. Teuwen, 2023, 12;(50):7579-7593
    Abstract
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  13. JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
    G. Yiasemis, N. Moriakov, C. I. Sánchez, J. Sonke, J. Teuwen, 2023
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  14. Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI
    Medical Image Analysis
    L. Han, T. Tan, T. Zhang, Y. Huang, X. Wang, Y. Gao, J. Teuwen, R. Mann, 2023
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  15. Clinicopathological and prognostic value of calcification morphology descriptors in ductal carcinoma in situ of the breast: a systematic review and meta-analysis
    Insights into Imaging
    M. M. Van Leeuwen, S. Doyle, A. W. Van Den Belt–Dusebout, S. Van Der Mierden, C. E. Loo, R. M. Mann, J. Teuwen, J. Wesseling, 2023, 14;(1)
    Abstract
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  16. Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors
    Journal of Medical Imaging
    S. D. Verboom, M. Caballo, J. Peters, J. Gommers, D. Van Den Oever, M. J. M. Broeders, J. Teuwen, I. Sechopoulos, 2023, 11;(01)
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2022

  1. FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
    T. O'Briain, C. Uribe, K. M. Yi, J. Teuwen, I. Sechopoulos, M. Bazalova-Carter, 2022
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  2. 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
    K. A. Wahid, S. Ahmed, R. He, L. V. Van Dijk, J. Teuwen, B. A. Mcdonald, V. Salama, A. S. Mohamed, T. Salzillo, C. Dede, N. Taku, S. Y. Lai, C. D. Fuller, M. A. Naser, 2022, 32
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  3. 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
    Y. Schirris, E. Gavves, I. Nederlof, H. M. Horlings, J. Teuwen, 2022, 79
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  4. Application of Deep Learning in Breast Cancer Imaging
    Seminars in Nuclear Medicine
    L. Balkenende, J. Teuwen, R. M. Mann, 2022, 52;(5):584-596
    Abstract
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  5. Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations
    Frontiers in Neuroscience
    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, 2022, 16
    Abstract
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  6. DIRECT: Deep Image REConstruction Toolkit
    Journal of Open Source Software
    G. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, 2022, 7;(73):4278
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  7. Deep MRI reconstruction with radial subsampling
    Medical Imaging 2022: Physics of Medical Imaging
    G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, 2022
    Abstract
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  8. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features
    Radiology
    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, 2022, 303;(1):54-62
    Abstract
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  9. Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI
    Diagnostics
    R. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen, R. M. Mann, 2022, 12;(7):1690
    Abstract
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  10. Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors
    Medical Imaging 2022: Image Processing
    S. D. Verboom, M. Caballo, M. J. M. Broeders, J. Teuwen, I. Sechopoulos, 2022
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  11. Tumor tracking in 4D CT images for adaptive radiotherapy
    Medical Imaging 2022: Image Processing
    P. S. Kronemeijer, E. Gavves, J. Sonke, J. Teuwen, 2022
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  12. Mammary duct detection using self-supervised encoders
    Medical Imaging 2022: Computer-Aided Diagnosis
    S. Doyle, F. Dal Canton, J. Wesseling, C. I. Sánchez, J. Teuwen, 2022
    Abstract
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  13. Breast imaging and deep learning: past, present, and future
    Advances in Magnetic Resonance Technology and Applications
    S. Eskreis-Winkler, J. Teuwen, S. Benson, 2022
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  14. Recurrent variational network: A deep learning inverse problem solver applied to the task of accelerated mri reconstruction
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    G. Yiasemis, J. Sonke, C. Sánchez, J. Teuwen, 2022
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  15. Federated learning enables big data for rare cancer boundary detection.
    Nature communications
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    Abstract
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