Publications of Bram van Ginneken
2019
Papers in international journals
- P. Bándi, M. Balkenhol, B. van Ginneken, J. van der Laak and G. Litjens, "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks", PeerJ, 2019;7:e8242.
- A. Patel, F. Schreuder, C. Klijn, M. Prokop, B. van Ginneken, H. Marquering, Y. Roos, M. Baharoglu, F. Meijer and R. Manniesing, "Intracerebral haemorrhage segmentation in non-contrast CT", Scientific Reports, 2019;9(1):17858.
- R. Philipsen, C. Sánchez, J. Melendez, W. Lew and B. van Ginneken, "Automated chest X-ray reading for tuberculosis in the Philippines to improve case detection: a cohort study", International Journal of Tuberculosis and Lung Disease, 2019;23(7):805-810.
- G. Aresta, C. Jacobs, T. Araujo, A. Cunha, I. Ramos, B. van Ginneken and A. Campilho, "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network", Scientific Reports, 2019;9(1):11591.
- C. Jacobs and B. van Ginneken, "Google's lung cancer AI: a promising tool that needs further validation", Nature Reviews Clinical Oncology, 2019;16(9):532-533.
- G. Chlebus, H. Meine, S. Thoduka, N. Abolmaali, B. van Ginneken, H. Hahn and A. Schenk, "Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections", PLoS One, 2019;14(5):e0217228.
- A. Schreuder, C. Jacobs, L. Gallardo-Estrella, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances", PLoS One, 2019;14(2):e0212756.
- N. Lessmann, B. van Ginneken, P. de Jong and I. Išgum, "Iterative fully convolutional neural networks for automatic vertebra segmentation and identification", Medical Image Analysis, 2019;53:142-155.
- W. Bulten, P. Bándi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. van der Laak, B. van Ginneken, C. Hulsbergen-van de Kaa and G. Litjens, "Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard", Scientific Reports, 2019;9(1).
- J. Charbonnier, E. Pompe, C. Moore, S. Humphries, B. van Ginneken, B. Make, E. Regan, J. Crapo, E. van Rikxoort and D. Lynch, "Airway wall thickening on CT: Relation to smoking status and severity of COPD", Respiratory Medicine, 2019;146:36-41.
- N. Lessmann, P. de Jong, C. Celeng, R. Takx, M. Viergever, B. van Ginneken and I. Išgum, "Sex Differences in Coronary Artery and Thoracic Aorta Calcification and Their Association With Cardiovascular Mortality in Heavy Smokers", JACC Cardiovascular Imaging, 2019;12:1808-1817.
- T. van den Heuvel, H. Petros, S. Santini, C. de Korte and B. van Ginneken, "Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries", Ultrasound in Medicine and Biology, 2019;45(3):773-785.
- B. van Ginneken, "Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?", Radiology, 2019;290:545-546.
- M. Tammemagi, A. Ritchie, S. Atkar-Khattra, B. Dougherty, C. Sanghera, J. Mayo, R. Yuan, D. Manos, A. McWilliams, H. Schmidt, M. Gingras, S. Pasian, L. Stewart, S. Tsai, J. M.Seely, P. Burrowes, R. Bhatia, E. A.Haider, C. Boylan, C. Jacobs, B. van Ginneken, M. Tsao, S. Lam and the Pan-Canadian Early Detection of Lung Cancer Study Group, "Predicting Malignancy Risk of Screen Detected Lung Nodules - Mean Diameter or Volume", Journal of Thoracic Oncology, 2019;14(2):203-211.
- S. van Riel, C. Jacobs, E. Scholten, R. Wittenberg, M. Winkler Wille, B. de Hoop, R. Sprengers, O. Mets, B. Geurts, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management", European Radiology, 2019;29(2):924-931.
- S. van de Leemput, M. Meijs, A. Patel, F. Meijer, B. van Ginneken and R. Manniesing, "Multiclass Brain Tissue Segmentation in 4D CT using Convolutional Neural Networks", IEEE Access, 2019;7(1):51557-51569.
- A. Patel, S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Image Level Training and Prediction: Intracranial Hemorrhage Identification in 3D Non-Contrast CT", IEEE Access, 2019;7(1):92355-92364.
- 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.
Preprints
- L. Maier-Hein, A. Reinke, M. Kozubek, A. L. Martel, T. Arbel, M. Eisenmann, A. Hanbuary, P. Jannin, H. Muller, S. Onogur, J. Saez-Rodriguez, B. van Ginneken, A. Kopp-Schneider and B. Landman, "BIAS: Transparent reporting of biomedical image analysis challenges", arXiv:1910.04071, 2019.
- P. Bilic, P. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C. Fu, X. Han, P. Heng, J. Hesser, S. Kadoury, T. Konopczynski, M. Le, C. Li, X. Li, J. Lipkova, J. Lowengrub, H. Meine, J. Moltz, C. Pal, M. Piraud, X. Qi, J. Qi, M. Rempfler, K. Roth, A. Schenk, A. Sekuboyina, E. Vorontsov, P. Zhou, C. Hulsemeyer, M. Beetz, F. Ettlinger, F. Gruen, G. Kaissis, F. Lohofer, R. Braren, J. Holch, F. Hofmann, W. Sommer, V. Heinemann, C. Jacobs, G. Humpire Mamani, B. van Ginneken, G. Chartrand, A. Tang, M. Drozdzal, A. Ben-Cohen, E. Klang, M. Amitai, E. Konen, H. Greenspan, J. Moreau, A. Hostettler, L. Soler, R. Vivanti, A. Szeskin, N. Lev-Cohain, J. Sosna, L. Joskowicz and B. Menze, "The Liver Tumor Segmentation Benchmark (LiTS)", arXiv:1901.04056, 2019.
- B. Liefers, J. Colijn, C. González-Gonzalo, T. Verzijden, P. Mitchell, C. Hoyng, B. van Ginneken, C. Klaver and C. Sánchez, "A deep learning model for segmentation of geographic atrophy to study its long-term natural history", arXiv:1908.05621, 2019.
- A. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, P. Bilic, P. Christ, R. Do, M. Gollub, J. Golia-Pernicka, S. Heckers, W. Jarnagin, M. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M. Cardoso, "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063, 2019.
- C. González-Gonzalo, B. Liefers, B. van Ginneken and C. Sánchez, "Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks", arXiv:1910.07373, 2019.
- M. Argus, C. Schaefer-Prokop, D. Lynch and B. van Ginneken, "Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans", arXiv:1909.12047, 2019.
- K. Murphy, S. Habib, S. Zaidi, S. Khowaja, A. Khan, J. Melendez, E. Scholten, F. Amad, S. Schalekamp, M. Verhagen, R. Philipsen, A. Meijers and B. van Ginneken, "Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system", arXiv:1903.03349, 2019.
Papers in conference proceedings
- B. Liefers, C. González-Gonzalo, C. Klaver, B. van Ginneken and C. Sánchez, "Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography", Medical Imaging with Deep Learning, 2019;102:337-346.
- T. van den Heuvel, C. de Korte and B. van Ginneken, "Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries", Medical Imaging with Deep Learning, 2019.
- T. van der Ouderaa, D. Worrall and B. van Ginneken, "Chest CT Super-resolution and Domain-adaptation using Memory-efficient 3D Reversible GANs", Medical Imaging with Deep Learning, 2019.
- N. Lessmann, J. Wolterink, M. Zreik, M. Viergever, B. van Ginneken and I. Isgum, "Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network", Medical Imaging with Deep Learning, 2019.
- E. Calli, E. Sogancioglu, E. Scholten, K. Murphy and B. van Ginneken, "Handling label noise through model confidence and uncertainty: application to chest radiograph classification", Medical Imaging, 2019(1).
- A. Hering, B. van Ginneken and S. Heldmann, "mlVIRNET: Multilevel Variational Image Registration Network", Medical Image Computing and Computer-Assisted Intervention, 2019;11769:257-265.
- E. Calli, K. Murphy, E. Sogancioglu and B. van Ginneken, "FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis", Medical Imaging with Deep Learning, 2019.
Abstracts
- C. Jacobs and B. van Ginneken, "Deep learning for detection and characterization of lung nodules", Annual Meeting of the European Society of Thoracic Imaging, 2019.
- M. Silva, G. Milanese, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, S. Sestini, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung Cancer Screening in NLST Eligibles: Tailoring Annual Low-Dose Computed Tomography by Post-Test Risk Stratification", Annual Meeting of the Radiological Society of North America, 2019.
- G. Chlebus, G. Humpire Mamani, A. Schenk, B. van Ginneken and H. Meine, "Mimicking radiologists to improve the robustness of deep-learning based automatic liver segmentation", Annual Meeting of the Radiological Society of North America, 2019.
- T. van den Heuvel, B. van Ginneken and C. de Korte, "Improving Maternal Care In Resource-Limited Settings Using A Low-Cost Ultrasound Device And Machine Learning", Dutch Bio-Medical Engineering Conference, 2019.
- H. van Zeeland, J. Meakin, B. Liefers, C. González-Gonzalo, A. Vaidyanathan, B. van Ginneken, C. Klaver and C. Sánchez, "EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images", Association for Research in Vision and Ophthalmology, 2019.
- C. Jacobs, E. Scholten, A. Schreuder, M. Prokop and B. van Ginneken, "An observer study comparing radiologists with the prize-winning lung cancer detection algorithms from the 2017 Kaggle Data Science Bowl", Annual Meeting of the Radiological Society of North America, 2019.
- M. Silva, G. Milanese, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung cancer risk after baseline round of screening: Only 20% of NLST eligibles require annual round", Annual Meeting of the European Society of Thoracic Imaging, 2019.
PhD theses
- F. Venhuizen, "Machine Learning for Quantification of Age-Related Macular Degeneration Imaging Biomarkers in Optical Coherence Tomography", PhD thesis, 2019.
- T. van den Heuvel, "Automated low-cost ultrasound: improving antenatal care in resource-limited settings", PhD thesis, 2019.
- L. Estrella, "Quantification of COPD biomarkers in thoracic CT scans", PhD thesis, 2019.
- E. Smit, "Feasibility of a single-acquisition CT stroke protocol", PhD thesis, 2019.
- R. Philipsen, "Automated chest radiography reading. Improvements, validation, and cost-effectiveness analysis", PhD thesis, 2019.
- N. Lessmann, "Machine Learning based quantification of extrapulmonary diseases in chest CT", PhD thesis, 2019.