Publications

Accepted or to appear

Papers in international journals

  1. M. Meijs, S.A. Pegge, K. Murayama, H.D. Boogaarts, M. Prokop, P.W. Willems, R. Manniesing and F.J. Meijer. "Color mapping of 4D-CTA for the detection of cranial arteriovenous shunts", American Journal of Neuroradiology. Abstract/PDF DOI PMID 31395664

  2. N. Lessmann, P.A. de Jong, C. Celeng, R.A.P. Takx, M.A. 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. Abstract/PDF DOI PMID 30660540

  3. M. Hermsen, T. de Bel, M. den Boer, E.J. Steenbergen, J. Kers, S. Florquin, J.J.T.H. Roelofs, M.D. Stegall, M.P. Alexander, B.H. Smith, B. Smeets, L.B. Hilbrands and J.A.W.M. van der Laak. "Deep-learning based histopathological assessment of kidney tissue", Journal of the American Society of Nephrology. Abstract

  4. C. Jacobs and B. van Ginneken. "Google’s lung cancer AI: a promising tool that needs further validation", Nature Reviews Clinical Oncology. Abstract/PDF DOI

  5. G. Napolitano, E. Lynge, M. Lillholm, I. Vejborg, C.H. van Gils, M. Nielsen and N. Karssemeijer. "Change in mammographic density across birth cohorts of Dutch breast cancer screening participants", International Journal of Cancer. Abstract/PDF DOI PMID 30762225

  6. D. Tellez, G. Litjens, J. van der Laak and F. Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence.

  7. D. Valkenburg, E.H. Runhart, N.M. Bax, B. Liefers, S.L. Lambertus, C.I. Sánchez, F.P. Cremers and C.B. Hoyng. "Highly variable disease courses in siblings with Stargardt disease", Ophthalmology. DOI

  8. J.C. van Zelst, T. Tan, R.M. Mann and N. Karssemeijer. "Validation of radiologists' findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software", Acta Radiologica:284185119858051. Abstract/PDF DOI PMID 31324132


2019

Papers in international journals

  1. S.C. 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;7:51557-51569. Abstract/PDF DOI

  2. A. Schreuder, C. Jacobs, L. Gallardo-Estrella, M. Prokop, C.M. 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:e0212756. Abstract/PDF DOI PMID 30789954

  3. N. Lessmann, B. van Ginneken, P.A. de Jong and I. Išgum. "Iterative fully convolutional neural networks for automatic vertebra segmentation and identification", Medical Image Analysis 2019;53:142-155. Abstract/PDF DOI arXiv PMID 30771712

  4. B. van Ginneken. "Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?", Radiology 2019;290:545-546. PDF DOI PMID 30422089

  5. M. Tammemagi, A.J. Ritchie, S. Atkar-Khattra, B. Dougherty, C. Sanghera, J.R. Mayo, R. Yuan, D. Manos, A.M. 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.-S. 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;14:203-211. Abstract/PDF DOI PMID 30368011

  6. L. Aprupe, G. Litjens, T.J. Brinker, J. van der Laak and N. Grabe. "Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks", PeerJ 2019;7:e6335. Abstract/PDF DOI PMID 30993030

  7. G. Aresta, C. Jacobs, T. Araújo, A. Cunha, I. Ramos, B. van Ginneken and A. Campilho. "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network", Nature Scientific Reports 2019;9(11591). Abstract/PDF DOI PMID 31406194

  8. M.C.A. Balkenhol, P. Bult, D. Tellez, W. Vreuls, P.C. Clahsen, F. Ciompi and J.A.W.M. van der Laak. "Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer", Cellular Oncology 2019. Abstract/PDF DOI PMID 30989469

  9. M.C.A. Balkenhol, D. Tellez, W. Vreuls, P.C. Clahsen, H. Pinckaers, F. Ciompi, P. Bult and J.A.W.M. van der Laak. "Deep learning assisted mitotic counting for breast cancer", Laboratory Investigation 2019. Abstract/PDF DOI PMID 31222166

  10. R.J. Becks, R. Manniesing, J. Vister, S. Pegge, S.C. Steens, E.J. van Dijk, M. Prokop and F.J. Meijer. "Brain CT Perfusion Improves Intracranial Vessel Occlusion Detection on CT Angiography", Journal of Neuroradiology 2019;46(2):124-129. Abstract/PDF DOI PMID 29625153

  11. 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", Nature Scientific Reports 2019;9(864). Abstract/PDF DOI arXiv PMID 30696866

  12. J.-P. Charbonnier, E. Pompe, C. Moore, S. Humphries, B. van Ginneken, B. Make, E. Regan, J.D. Crapo, E.M. van Rikxoort and D.A. Lynch. "Airway wall thickening on CT: Relation to smoking status and severity of COPD", Respiratory Medicine 2019;146:36-41. Abstract/PDF DOI PMID 30665516

  13. G. Chlebus, H. Meine, S. Thoduka, N. Abolmaali, B. van Ginneken, H.K. 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:e0217228. Abstract/PDF DOI PMID 31107915

  14. M.U. Dalmış, A. Gubern-Merida, 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. Abstract/PDF DOI PMID 30652985

  15. R.P. Finger, S. Schmitz-Valckenberg, M. Schmid, G.S. Rubin, H. Dunbar, A. Tufail, D.P. Crabb, A. Binns, C.I. Sánchez, P. Margaron, G. Normand, M.K. Durbin, U.F.O. Luhmann, P. Zamiri, J. Cunha-Vaz, F. Asmus, F.G. Holz and on behalf of the MACUSTAR consortium. "MACUSTAR: Development and Clinical Validation of Functional, Structural, and Patient-Reported Endpoints in Intermediate Age-Related Macular Degeneration" 2019;241:61-72. Abstract/PDF DOI PMID 30153664

  16. J.J. Gomez-Valverde, A. Anton, G. Fatti, B. Liefers, A. Herranz, A. Santos, C.I. Sanchez and M.J. Ledesma-Carbayo. "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning", Biomedical Optics Express 2019;10(2):892-913. Abstract/PDF DOI

  17. O. Geessink, A. Baidoshvili, J. Klaase, B. Ehteshami Bejnordi, G. Litjens, G. van Pelt, W. Mesker, I. Nagtegaal, F. Ciompi and J. van der Laak. "Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer", Cellular Oncology 2019:1-11. Abstract/PDF DOI PMID 30825182

  18. T.J. Heesterbeek, E.K. de Jong, I.E. Acar, J.M.M. Groenewoud, B. Liefers, C.I. Sánchez, T. Peto, C.B. Hoyng, D. Pauleikhoff, H.W. Hense and A.I. den Hollander. "Genetic risk score has added value over initial clinical grading stage in predicting disease progression in age-related macular degeneration", Nature Scientific Reports 2019;9:6611. Abstract/PDF DOI PMID 31036867

  19. T.L.A. van den Heuvel, H. Petros, S. Santini, C.L. 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. Abstract/PDF DOI PMID 30573305

  20. S.C. 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/PDF DOI

  21. E.M. van Leijsen, M.I. Bergkamp, I.W. van Uden, S. Cooijmans, M. Ghafoorian, H.M. van der Holst, D.G. Norris, R.P. Kessels, B. Platel, A.M. Tuladhar and F.-E. de Leeuw. "Cognitive consequences of regression of cerebral small vessel disease", European Stroke Journal 2019;4:85-89. Abstract/PDF DOI PMID 31165098

  22. G. Litjens, F. Ciompi, J.M. Wolterink, B.D. de Vos, T. Leiner, J. Teuwen and I. Išgum. "State-of-the-Art Deep Learning in Cardiovascular Image Analysis.", JACC Cardiovascular Imaging 2019;12:1549-1565. Abstract/PDF DOI PMID 31395244

  23. M.C. Maas, G.J.S. Litjens, A.J. Wright, U.I. Attenberger, M.A. Haider, T.H. Helbich, B. Kiefer, K.J. Macura, D.J.A. Margolis, A.R. Padhani, K.M. Selnæs, G.M. Villeirs, J.J. Fütterer and T.W.J. Scheenen. "A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach", Investigative Radiology 2019. Abstract/PDF DOI PMID 30946180

  24. A. Patel, S.C. 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;7:92355-92364. Abstract/PDF DOI

  25. S.J. van Riel, C. Jacobs, E.T. Scholten, R. Wittenberg, M.M. Winkler Wille, B. de Hoop, R. Sprengers, O.M. 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. Abstract/PDF DOI PMID 30066248

  26. A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T.H. Helbich, M. Chevalier, T. Mertelmeier, M.G. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos and R.M. Mann. "Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study", European Radiology 2019;29:4825-4832. Abstract/PDF DOI PMID 30993432

  27. S. Saadatmand, H.A. Geuzinge, E.J.T. Rutgers, R.M. Mann, D.B.W. de Roy van Zuidewijn, H.M. Zonderland, R.A.E.M. Tollenaar, M.B.I. Lobbes, M.G.E.M. Ausems, M. van 't Riet, M.J. Hooning, I. Mares-Engelberts, E.J.T. Luiten, E.A.M. Heijnsdijk, C. Verhoef, N. Karssemeijer, J.C. Oosterwijk, I.-M. Obdeijn, H.J. de Koning, M.M.A. Tilanus-Linthorst and F. study group. "MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial", Lancet Oncology 2019;20:1136-1147. Abstract/PDF DOI PMID 31221620

  28. W.B.G. Sanderink, B.I. Laarhuis, L.J.A. Strobbe, I. Sechopoulos, P. Bult, N. Karssemeijer and R.M. Mann. "A systematic review on the use of the breast lesion excision system in breast disease", Insights into Imaging 2019;10:49. Abstract/PDF DOI PMID 31049740

  29. V. Schreur, A. de Breuk, F.G. Venhuizen, C.I. Sánchez, C.J. Tack, B.J. Klevering, E.K. de Jong and C.B. Hoyng. "Retinal hyperreflective foci in type 1 diabetes mellitus", Retina 2019. Abstract/PDF DOI PMID 31356496

  30. V. Schreur, A. Domanian, B. Liefers, F.G. Venhuizen, B.J. Klevering, C.B. Hoyng, E.K. de Jong and T. Theelen. "Morphological and topographical appearance of microaneurysms on optical coherence tomography angiography", British Journal of Ophthalmology 2019;103(5):630-635. Abstract DOI PMID 29925511

  31. M. Veta, Y.J. Heng, N. Stathonikos, B.E. Bejnordi, F. Beca, T. Wollmann, K. Rohr, M.A. Shah, D. Wang, M. Rousson, M. Hedlund, D. Tellez, F. Ciompi, E. Zerhouni, D. Lanyi, M. Viana, V. Kovalev, V. Liauchuk, H.A. Phoulady, T. Qaiser, S. Graham, N. Rajpoot, E. Sjöblom, J. Molin, K. Paeng, S. Hwang, S. Park, Z. Jia, E.I.-C. Chang, Y. Xu, A.H. Beck, P.J. van Diest and J.P.W. Pluim. "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge", Medical Image Analysis 2019;54(5):111-121. Abstract/PDF DOI PMID 30861443


Preprints

  1. P. Bilic, P.F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.-W. Fu, X. Han, P.-A. Heng, J. Hesser, S. Kadoury, T. Konopczynski, M. Le, C. Li, X. Li, J. Lipkovà, J. Lowengrub, H. Meine, J.H. Moltz, C. Pal, M. Piraud, X. Qi, J. Qi, M. Rempfler, K. Roth, A. Schenk, A. Sekuboyina, E. Vorontsov, P. Zhou, C. Hülsemeyer, M. Beetz, F. Ettlinger, F. Gruen, G. Kaissis, F. Lohöfer, R. Braren, J. Holch, F. Hofmann, W. Sommer, V. Heinemann, C. Jacobs, G.E. Humpire Mamani, B. van Ginneken, G. Chartrand, A. Tang, M. Drozdzal, A. Ben-Cohen, E. Klang, M.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.H. Menze. "The Liver Tumor Segmentation Benchmark (LiTS)", arXiv:1901.04056 2019. Abstract

  2. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. Hulsbergen-van de Kaa and G. Litjens. "Automated Gleason Grading of Prostate Biopsies using Deep Learning", arXiv:1907.07980 2019. Abstract arXiv

  3. C. González-Gonzalo, V. Sánchez-Gutiérrez, P. Hernández-Martínez, I. Contreras, Y.T. Lechanteur, A. Domanian, B. van Ginneken and C.I. Sánchez. "Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration", arXiv:1903.09555 2019. Abstract arXiv

  4. S. Hu, D. Worrall, S. Knegt, B. Veeling, H. Huisman and M. Welling. "Supervised uncertainty quantification for segmentation with multiple annotations", arXiv preprint arXiv:1907.01949 2019. Abstract

  5. B. Liefers, J.M. Colijn, C. González-Gonzalo, T. Verzijden, P. Mitchell, C.B. Hoyng, B. van Ginneken, C.C. Klaver and C.I. Sánchez. "A deep learning model for segmentation of geographic atrophy to study its long-term natural history", arXiv:1908.05621 2019. Abstract/PDF arXiv

  6. K. Murphy, S.S. Habib, S.M.A. Zaidi, S. Khowaja, A. Khan, J. Melendez, E.T. Scholten, F. Amad, S. Schalekamp, M. Verhagen, R.H.H.M. 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. Abstract/PDF arXiv

  7. A.L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B.A. Landman, G. Litjens, B. Menze, O. Ronneberger, R.M. Summers, P. Bilic, P.F. Christ, R.K.G. Do, M. Gollub, J. Golia-Pernicka, S.H. Heckers, W.R. Jarnagin, M.K. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M.J. Cardoso. "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063 2019. Abstract/PDF arXiv


Papers in conference proceedings

  1. E. Calli, E. Sogancioglu, E.T. Scholten, K. Murphy and B. van Ginneken. "Handling label noise through model confidence and uncertainty: application to chest radiograph classification", in: Medical Imaging of Proceedings of the SPIE, 2019. Abstract/PDF DOI

  2. T. de Bel, M. Hermsen, J. Kers, J. van der Laak and G. Litjens. "Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology", in: Medical Imaging with Deep Learning, 2019. Abstract URL

  3. J.-M. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi. "Learning from sparsely annotated data for semantic segmentation in histopathology images", in: Medical Imaging with Deep Learning, volume 102, 2019, pages 81-94. Abstract/PDF URL

  4. 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", in: Medical Imaging of SPIE, 2019. Abstract/PDF DOI

  5. E. Calli, K. Murphy, E. Sogancioglu and B. van Ginneken. "FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF URL

  6. M. Hosseinzadeh, P. Brand and H. Huisman. "Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF URL

  7. N. Lessmann, J.M. Wolterink, M. Zreik, M.A. Viergever, B. van Ginneken and I. Išgum. "Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network", in: Medical Imaging with Deep Learning, 2019. Abstract URL

  8. B. Liefers, C. González-Gonzalo, C. Klaver, B. van Ginneken and C.I. Sánchez. "Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography", in: Medical Imaging with Deep Learning, volume 102 of Proceedings of Machine Learning Research, 2019, pages 337-346. Abstract/PDF URL

  9. C. Mercan, M. Balkenhol, J. van der Laak and F. Ciompi. "From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNet", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF URL

  10. N. Moriakov, K. Michielsen, R. Mann, J. Adler, I. Sechopolous and J. Teuwen. "Deep learning framework for digital breast tomosynthesis reconstruction", in: Medical Imaging of SPIE, 2019. Abstract/PDF arXiv

  11. H. Pinckaers, W. Bulten and G. Litjens. "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", in: Medical Imaging of Proceedings of the SPIE, 2019. Abstract/PDF DOI

  12. J. van Vugt, E. Marchiori, R. Mann, A. Gubern-Mérida, N. Moriakov and J. Teuwen. "Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation", in: Medical Imaging of SPIE, 2019. Abstract/PDF DOI


Abstracts

  1. J.-M. Bokhorst, H. Dawson, A. Blank, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, M. Urbanowicz, S. Brockmoeller, J.-F. Flejou, L. Rijstenberg, J. van der Laak, F. Ciompi and I. Nagtegaal. "Assessment of tumor buds in colorectal cancer. A large-scale international digital observer study", in: European Congress of Pathology, 2019. Abstract

  2. W. Bulten, H. Pinckaers, C. Hulsbergen-van de Kaa and G. Litjens. "Automated Gleason Grading of Prostate Biopsies Using Deep Learning", in: United States and Canadian Academy of Pathology (USCAP) 108th Annual Meeting, 2019. Abstract

  3. K. Dercksen, W. Bulten and G. Litjens. "Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification", in: Medical Imaging with Deep Learning, 2019. Abstract arXiv

  4. J. Engelberts, C. González-Gonzalo, C.I. Sánchez and M.J. van Grinsven. "Automatic Segmentation of Drusen and Exudates on Color Fundus Images using Generative Adversarial Networks", in: Association for Research in Vision and Ophthalmology, 2019. Abstract

  5. C. González-Gonzalo, B. Liefers, A. Vaidyanathan, H. van Zeeland, C.C.W. Klaver and C.I. Sánchez. "Opening the “black box” of deep learning in automated screening of eye diseases", in: Association for Research in Vision and Ophthalmology, 2019. Abstract

  6. T.L.A. van den Heuvel, B. van Ginneken and C.L. de Korte. "Improving Maternal Care In Resource-Limited Settings Using A Low-Cost Ultrasound Device And Machine Learning", in: Dutch Bio-Medical Engineering Conference, 2019. Abstract/PDF

  7. T.L.A. van den Heuvel, C.L. de Korte and B. van Ginneken. "Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries", in: Medical Imaging with Deep Learning, 2019. Abstract/PDF

  8. B. Liefers, J. Colijn, C. González-Gonzalo, A. Vaidyanathan, H. van Zeeland, P. Mitchell, C. Klaver and C.I. Sánchez. "Prediction of areas at risk of developing geographic atrophy in color fundus images using deep learning", in: Association for Research in Vision and Ophthalmology, 2019. Abstract

  9. 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", in: Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2019. Abstract

  10. D. Valkenburg, E. Runhart, B. Liefers, S. Lambertus, C.I. Sánchez, F.P. Cremers, B. Nathalie M and C.C.B. Hoyng. "Familial discordance in disease phenotype in siblings with Stargardt disease", in: Association for Research in Vision and Ophthalmology, 2019. Abstract

  11. H. van Zeeland, J. Meakin, B. Liefers, C. González-Gonzalo, A. Vaidyanathan, B. van Ginneken, C.C.W. Klaver and C.I. Sánchez. "EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images", in: Association for Research in Vision and Ophthalmology, 2019. Abstract


Theses

  1. D. Geijs. "Tumor segmentation in fluorescent TNBC immunohistochemical multiplex images using deep learning", Masters thesis, University of Twente, 2019. Abstract/PDF

  2. J. Winkens. "Out-of-distribution detection for computational pathology with multi-head ensembles", Masters thesis, University of Amsterdam, 2019. Abstract/PDF

  3. T. van der Ouderaa. "Reversible Networks for Memory-efficient Image-to-Image Translation in 3D Medical Imaging", Masters thesis, University of Amsterdam, 2019. Abstract/PDF

  4. L.G. Estrella. "Quantification of COPD biomarkers in thoracic CT scans", PhD thesis, Radboud University, Nijmegen, 2019. Abstract/PDF URL

  5. T.L.A. van den Heuvel. "Automated low-cost ultrasound: improving antenatal care in resource-limited settings", PhD thesis, Radboud University, Nijmegen, 2019. Abstract/PDF URL

  6. N. Lessmann. "Machine Learning based quantification of extrapulmonary diseases in chest CT", PhD thesis, Utrecht University, The Netherlands, 2019. Abstract/PDF URL

  7. R. Philipsen. "Automated chest radiography reading. Improvements, validation, and cost-effectiveness analysis", PhD thesis, 2019. Abstract/PDF URL

  8. A.R. Ruiz. "Artificial intelligence & tomosynthesis for breast cancer detection", PhD thesis, Radboud University, Nijmegen, 2019. Abstract/PDF URL

  9. E. Smit. "Feasibility of a single-acquisition CT stroke protocol", PhD thesis, University of Utrecht, The Netherlands, 2019. Abstract/PDF

  10. J. van Zelst. "Automated 3D breast ultrasound Advances in breast cancer detection, diagnosis and screening", PhD thesis, Radboud University, Nijmegen, 2019. Abstract/PDF