Publications of Jonas Teuwen

2018

Papers in international journals

1. 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

Preprints

1. J. Teuwen and P. Urbach, "On Maximum Focused Electric Energy in Bounded Regions", arXiv:1801.02450, 2018.
   Abstract arXiv
2. 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

Papers in conference proceedings

1. 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
2. T. de Moor, A. Rodriguez-Ruiz, R. Mann, A. Gubern Mérida and 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.
   Abstract DOI
3. 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
4. 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
5. 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

Abstracts

1. 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