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A. Sekuboyina, M. Husseini, A. Bayat, M. Löffler, H. Liebl, H. Li, G. Tetteh, J. Kukačka, C. Payer, D. Stern, M. Urschler, M. Chen, D. Cheng, N. Lessmann, Y. Hu, T. Wang, D. Yang, D. Xu, F. Ambellan, T. Amiranashvili, M. Ehlke, H. Lamecker, S. Lehnert, M. Lirio, N. de Olaguer, H. Ramm, M. Sahu, A. Tack, S. Zachow, T. Jiang, X. Ma, C. Angerman, X. Wang, K. Brown, A. Kirszenberg, É. Puybareau, D. Chen, Y. Bai, B. Rapazzo, T. Yeah, A. Zhang, S. Xu, F. Hou, Z. He, C. Zeng, Z. Xiangshang, X. Liming, T. Netherton, R. Mumme, L. Court, Z. Huang, C. He, L. Wang, S. Ling, L. Huynh, N. Boutry, R. Jakubicek, J. Chmelik, S. Mulay, M. Sivaprakasam, J. Paetzold, S. Shit, I. Ezhov, B. Wiestler, B. Glocker, A. Valentinitsch, M. Rempfler, B. Menze and J. Kirschke, "VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images",
arXiv:2001.09193,
2020.
C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. van der Laak and F. Ciompi, "Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer",
arXiv:2012.04974,
2020.
A. Meyer, G. Chlebus, M. Rak, D. Schindele, M. Schostak, B. van Ginneken, A. Schenk, H. Meine, H. Hahn, A. Schreiber and C. Hansen, "Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI",
arXiv:2009.11120,
2020.
E. García, Y. Diez, A. Oliver, N. Karssemeijer, J. Martí, R. Martí and O. Diaz, "Evaluation of elastic parameters for breast compression using a MRI-mammography registration approach",
15th International Workshop on Breast Imaging (IWBI2020),
2020.
B. Lassen-Schmidt, A. Hering, S. Krass and H. Meine, "Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function",
Medical Imaging with Deep Learning,
2020.
A. Saha, P. Prasad and A. Thabit, "Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation",
IEEE International Symposium on Biomedical Imaging,
2020:2014-2017.
H. Altun, G. Chlebus, C. Jacobs, H. Meine, B. van Ginneken and H. Hahn, "Feasibility of End-To-End Trainable Two-Stage U-Net for Detection of Axillary Lymph Nodes in Contrast-Enhanced CT Based Scans on Sparse Annotations",
Medical Imaging,
2020:113141C.
J. Linmans, J. van der Laak and G. Litjens, "Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks",
Medical Imaging with Deep Learning,
2020:465-478.
X. Yu, B. Lou, B. Shi, D. Winkel, N. Arrahmane, M. Diallo, T. Meng, H. von Busch, R. Grimm, B. Kiefer, D. Comaniciu, A. Kamen, H. Huisman, A. Rosenkrantz, T. Penzkofer, I. Shabunin, M. Choi, Q. Yang and D. Szolar, "False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans",
IEEE International Symposium on Biomedical Imaging,
2020.
L. van Eekelen, H. Pinckaers, K. Hebeda and G. Litjens, "Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images",
Medical Imaging,
2020;11320:113200B.
D. Tellez, D. Hoppener, C. Verhoef, D. Grunhagen, P. Nierop, M. Drozdzal, J. van der Laak and F. Ciompi, "Extending Unsupervised Neural Image Compression With Supervised Multitask Learning",
Medical Imaging with Deep Learning,
2020.
C. Mercan, G. Reijnen-Mooij, D. Martin, J. Lotz, N. Weiss, M. van Gerven and F. Ciompi, "Virtual staining for mitosis detection in Breast Histopathology",
IEEE International Symposium on Biomedical Imaging,
2020:1770-1774.
Z. Swiderska-Chadaj, E. Stoelinga, A. Gertych and F. Ciompi, "Multi-Patch Blending improves lung cancer growth pattern segmentation in whole-slide images",
IEEE International Conference on Computational Problems of Electrical Engineering,
2020.
K. Faryna, K. Koschmieder, M. Paul, T. van den Heuvel, A. van der Eerden, R. Manniesing and B. van Ginneken, "Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation",
Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS),
2020.
A. Hering and S. Heldmann, "mlVIRNET: Improved Deep Learning Registration Using a Coarse to Fine Approach to Capture all Levels of Motion",
Bildverarbeitung für die Medizin,
2020:175.
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.
Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning",
Medical Imaging,
2020;11320:1132010.
N. Moriakov, J. Adler and J. Teuwen, "Kernel of CycleGAN as a principal homogeneous space",
International Conference on Learning Representations,
2020.
K. Faryna, F. Tushar, V. D'Anniballe, R. Hou, G. Rubin and J. Lo, "Attention-guided classification of abnormalities in semi-structured computed tomography reports",
Medical Imaging,
2020;11314:397 - 403.
A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI",
Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS),
2020.
A. Saha, F. Tushar, K. Faryna, V. D'Anniballe, R. Hou, M. Mazurowski, G. Rubin and J. Lo, "Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features",
Medical Imaging,
2020;11314:39 - 44.
C. Balta, A. Rodriguez-Ruiz, C. Mieskes, N. Karssemeijer and S. Heywang-Köbrunner, "Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?",
15th International Workshop on Breast Imaging (IWBI2020),
2020.
Z. Swiderska-Chadaj, K. Nurzynska, G. Bartlomiej, K. Grunberg, L. van der Woude, M. Looijen-Salamon, A. Walts, T. Markiewicz, F. Ciompi and A. Gertych, "A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma",
Medical Imaging,
2020;11320:113200D.
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.
C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning",
European Congress of Pathology,
2020.
B. Liefers, P. Taylor, C. González-Gonzalo, A. Tufail and C. Sánchez, "Achieving expert level performance in quantifying 13 distinctive features of neovascular age-related macular degeneration on optical coherence tomography",
European Society of Retina Specialists,
2020.
M. Balkenhol, P. Bult, D. Tellez, W. Vreuls, P. Clahsen, F. Ciompi and J. der Laak, "Deep learning enables fully automated mitotic density assessment in breast cancer histopathology",
European Journal of Cancer,
2020.
J. Bokhorst, I. Nagtegaal, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and F. Ciompi, "Deep learning based tumor bud detection in pan-cytokeratin stained colorectal cancer whole-slide images",
European Congress of Pathology,
2020.
K. Venkadesh, A. Setio, Z. Saghir, B. van Ginneken and C. Jacobs, "Deep Learning for Lung Nodule Malignancy Prediction: Comparison With Clinicians and the Brock Model on an Independent Dataset From a Large Lung Screening Trial",
Annual Meeting of the Radiological Society of North America,
2020.
L. Studer, J. Bokhorst, I. Zlobec, A. Lugli, A. Fischer, F. Ciompi, J. van der Laak, I. Nagtegaal and H. Dawson, "Validation of computer-assisted tumour-bud and T-cell detection in pT1 colorectal cancer",
European Congress of pathology,
2020.
J. Bokhorst, F. Ciompi, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and I. Nagtegaal, "Computer-assisted hot-spot selection for tumor budding assessment in colorectal cancer",
European Congress of Pathology,
2020.
A. Ardu, B. Liefers, C. de Vente, C. González-Gonzalo, C. Klaver and C. Sánchez, "Artificial Intelligence for the Classification and Quantification of Reticular Pseudodrusen in Multimodal Retinal Images",
European Society of Retina Specialists,
2020.
D. Grob, S. Schalekamp, L. Oostveen, W. van der Woude, C. Jacobs, M. Prokop, I. Sechopoulos and M. Brink, "Pulmonary nodule growth: can follow-up be shortened with a high-end or an ultra-high-resolution CT scanner?",
European Congress of Radiology,
2020.
K. van Leeuwen, S. Schalekamp, M. Rutten, B. van Ginneken and M. de Rooij, "Scientific Evidence for 100 Commercially Available Artificial Intelligence Tools for Radiology: A Systematic Review",
Annual Meeting of the Radiological Society of North America,
2020.
C. González-Gonzalo, S. Wetstein, G. Bortsova, B. Liefers, B. van Ginneken and C. Sánchez, "Are adversarial attacks an actual threat for deep learning systems in real-world eye disease screening settings?",
European Society of Retina Specialists,
2020.
C. de Vente, M. van Grinsven, S. De Zanet, A. Mosinska, R. Sznitman, C. Klaver and C. Sánchez, "Estimating Uncertainty of Deep Neural Networks for Age-related Macular Degeneration Grading using Optical Coherence Tomography",
Association for Research in Vision and Ophthalmology,
2020.
T. Haddad, J. Bokhorst, L. van den Dobbelsteen, F. Simmer, J. van der Laak and I. Nagtegaal, "Characterisation of the tumour-host interface as a prognostic factor through deep learning systems",
United European Gastroenterology Journal,
2020.
T. Riepe, M. Hosseinzadeh, P. Brand and H. Huisman, "Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation",
Annual Meeting of the International Society for Magnetic Resonance in Medicine,
2020.
M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence",
PhD thesis,
2020.