Publications

2021

Papers in international journals

  1. A. Schreuder, C. Jacobs, N. Lessmann, M. Broeders, M. Silva, I. Išgum, P. de Jong, M. van den Heuvel, N. Sverzellati, M. Prokop, U. Pastorino, C. Schaefer-Prokop and B. van Ginneken, "Scan-based competing death risk model for reevaluating lung cancer computed tomography screening eligibility", European Respiratory Journal, 2021.
    Abstract DOI PMID
  2. C. Jacobs, A. Schreuder, S. van Riel, 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, "Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement", Radiology: Imaging Cancer, 2021;3(5):e200160.
    Abstract DOI PMID
  3. M. Dekker, F. Waissi, M. Silvis, J. Bennekom, A. Schoneveld, R. de Winter, I. Isgum, N. Lessmann, B. Velthuis, G. Pasterkamp, A. Mosterd, L. Timmers and D. de Kleijn, "High Levels of Osteoprotegerin Are Associated with Coronary Artery Calcification in Patients Suspected of a Chronic Coronary Syndrome", Nature Scientific Reports, 2021;11(1):18946.
    Abstract DOI PMID Algorithm
  4. A. Schreuder, M. Prokop, E. Scholten, O. Mets, K. Chung, F. Mohamed Hoesein, C. Jacobs and C. Schaefer-Prokop, "CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?", Cancers, 2021;13(11):2812.
    Abstract DOI PMID
  5. E. Callı, E. Sogancioglu, B. van Ginneken, K. van Leeuwen and K. Murphy, "Deep learning for chest X-ray analysis: A survey", 2021;72:102125.
    Abstract DOI PMID
  6. A. Schreuder, E. Scholten, B. van Ginneken and C. Jacobs, "Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?", Translational Lung Cancer Research, 2021;10(5):2378-2388.
    Abstract DOI PMID
  7. M. Hermsen, V. Volk, J. Bräsen, D. Geijs, W. Gwinner, J. Kers, J. Linmans, N. Schaadt, J. Schmitz, E. Steenbergen, Z. Swiderska-Chadaj, B. Smeets, L. Hilbrands and J. van der Laak, "Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning", Laboratory Investigation, 2021;101(8):970-982.
    Abstract DOI PMID
  8. K. Venkadesh, A. Setio, A. Schreuder, E. Scholten, K. Chung, M. W Wille, Z. Saghir, B. van Ginneken, M. Prokop and C. Jacobs, "Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.", Radiology, 2021;300(2):438-447.
    Abstract DOI PMID Algorithm
  9. T. Penzkofer, A. Padhani, B. Turkbey, M. Haider, H. Huisman, J. Walz, G. Salomon, I. Schoots, J. Richenberg, G. Villeirs, V. Panebianco, O. Rouviere, V. Logager and J. Barentsz, "ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.", European Radiology, 2021.
    Abstract DOI PMID
  10. J. van der Laak, G. Litjens and F. Ciompi, "Deep learning in histopathology: the path to the clinic.", Nature Medicine, 2021;27(5):775-784.
    Abstract DOI PMID
  11. B. de Vos, N. Lessmann, P. de Jong and I. Išgum, "Deep Learning–Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT", Radiology: Cardiothoracic Imaging, 2021;3(2):e190219.
    Abstract DOI PMID
  12. R. Gal, S. van Velzen, M. Hooning, M. Emaus, F. van der Leij, M. Gregorowitsch, E. Blezer, S. Gernaat, N. Lessmann, M. Sattler, T. Leiner, P. de Jong, A. Teske, J. Verloop, J. Penninkhof, I. Vaartjes, H. Meijer, J. van Tol-Geerdink, J. Pignol, D. van den Bongard, I. Išgum and H. Verkooijen, "Identification of Risk of Cardiovascular Disease by Automatic Quantification of Coronary Artery Calcifications on Radiotherapy Planning CT Scans in Patients With Breast Cancer", JAMA Oncology, 2021;7(7):1024-1032.
    Abstract DOI PMID Algorithm
  13. A. Schreuder, O. Mets, C. Schaefer-Prokop, C. Jacobs and M. Prokop, "Microsimulation modeling of extended annual CT screening among lung cancer cases in the National Lung Screening Trial", Lung Cancer, 2021;156:5-11.
    Abstract DOI PMID
  14. K. van Leeuwen, S. Schalekamp, M. Rutten, B. van Ginneken and M. de Rooij, "Artificial intelligence in radiology: 100 commercially available products and their scientific evidence", European Radiology, 2021;31:3797–3804.
    Abstract DOI PMID
  15. F. Faita, T. Oranges, N. Di Lascio, F. Ciompi, S. Vitali, G. Aringhieri, A. Janowska, M. Romanelli and V. Dini, "Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.", Experimental Dermatology, 2021.
    Abstract DOI PMID
  16. H. Pinckaers, W. Bulten, J. der Van Laak and G. Litjens, "Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels.", IEEE Transactions on Medical Imaging, 2021;PP.
    Abstract DOI PMID
  17. M. Velema, L. Canu, T. Dekkers, A. Hermus, H. Timmers, L. Schultze Kool, H. Groenewoud, C. Jacobs, J. Deinum and S. Investigators, "Volumetric evaluation of CT images of adrenal glands in primary aldosteronism.", Journal of endocrinological investigation, 2021;44(11):2359-2366.
    Abstract DOI PMID
  18. T. de Bel, J. Bokhorst, J. van der Laak and G. Litjens, "Residual cyclegan for robust domain transformation of histopathological tissue slides.", Medical Image Analysis, 2021;70:102004.
    Abstract DOI PMID
  19. M. Balkenhol, F. Ciompi, Ż. Świderska-Chadaj, R. van de Loo, M. Intezar, I. Otte-Höller, D. Geijs, J. Lotz, N. Weiss, T. de Bel, G. Litjens, P. Bult and J. van der Laak, "Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.", BREAST, 2021;56:78-87.
    Abstract DOI PMID
  20. O. Turner, B. Knight, A. Zuraw, G. Litjens and D. Rudmann, "Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.", TP, 2021;49(4):714-719.
    Abstract DOI PMID
  21. A. Schreuder, C. Jacobs, N. Lessmann, M. Broeders, M. Silva, I. Išgum, P. de Jong, N. Sverzellati, M. Prokop, U. Pastorino, C. Schaefer-Prokop and B. van Ginneken, "Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening", European Respiratory Journal, 2021;58(3):2003386.
    Abstract DOI PMID
  22. D. Grob, L. Oostveen, C. Jacobs, E. Scholten, M. Prokop, C. Schaefer-Prokop, I. Sechopoulos and M. Brink, "Pulmonary nodule enhancement in subtraction CT and dual-energy CT: A comparison study", European Journal of Radiology, 2021;134:109443.
    Abstract DOI PMID
  23. M. van Rijthoven, M. Balkenhol, K. Silina, J. van der Laak and F. Ciompi, "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images", Medical Image Analysis, 2021;68:101890.
    Abstract DOI PMID GitHub Algorithm
  24. J. Bartstra, F. Draaisma, S. Zwakenberg, N. Lessmann, J. Wolterink, Y. van der Schouw, P. de Jong and J. Beulens, "Six months vitamin K treatment does not affect systemic arterial calcification or bone mineral density in diabetes mellitus 2", European Journal of Nutrition, 2021;60:1691-1699.
    Abstract DOI PMID
  25. N. Lessmann, C. Sánchez, L. Beenen, L. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. van Everdingen, P. Gerke, B. Geurts, H. Gietema, M. Groeneveld, L. van Harten, N. Hendrix, W. Hendrix, H. Huisman, I. Isgum, C. Jacobs, R. Kluge, M. Kok, J. Krdzalic, B. Lassen-Schmidt, K. van Leeuwen, J. Meakin, M. Overkamp, T. van Rees Vellinga, E. van Rikxoort, R. Samperna, C. Schaefer-Prokop, S. Schalekamp, E. Scholten, C. Sital, L. Stöger, J. Teuwen, K. Vaidhya Venkadesh, C. de Vente, M. Vermaat, W. Xie, B. de Wilde, M. Prokop and B. van Ginneken, "Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence", Radiology, 2021;298(1):E18-E28.
    Abstract DOI PMID Algorithm
  26. 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, 2021;43(2):567-578.
    Abstract DOI PMID
  27. E. Callı, K. Murphy, S. Kurstjens, T. Samson, R. Herpers, H. Smits, M. Rutten and B. van Ginneken, "Deep learning with robustness to missing data: A novel approach to the detection of COVID-19", 2021;16(7):e0255301.
    Abstract DOI
  28. K. Leeuwen, F. Meijer, S. Schalekamp, M. Rutten, E. Dijk, B. Ginneken, T. Govers and M. Rooij, "Cost ‑ effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke : an early health technology assessment", Insights into Imaging, 2021;12:133.
    Abstract DOI
  29. T. Perik, E. van Genugten, E. Aarntzen, E. Smit, H. Huisman and J. Hermans, "Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review", Abdominal Radiology, 2021.
    Abstract DOI
  30. N. Hendrix, E. Scholten, B. Vernhout, S. Bruijnen, B. Maresch, M. de Jong, S. Diepstraten, S. Bollen, S. Schalekamp, M. de Rooij, A. Scholtens, W. Hendrix, T. Samson, L. Sharon Ong, E. Postma, B. van Ginneken and M. Rutten, "Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs", Radiology: Artificial Intelligence, 2021:e200260.
    Abstract DOI Algorithm
  31. M. Hosseinzadeh, A. Saha, P. Brand, I. Slootweg, M. de Rooij and H. Huisman, "Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge", European Radiology, 2021.
    Abstract DOI
  32. J. Twilt, K. van Leeuwen, H. Huisman, J. Fütterer and M. de Rooij, "Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review", Diagnostics, 2021;11(6).
    Abstract DOI
  33. D. Winkel, A. Tong, B. Lou, A. Kamen, D. Comaniciu, J. Disselhorst, A. Rodr\'ıguez-Ruiz, H. Huisman, D. Szolar, I. Shabunin, M. Choi, P. Xing, T. Penzkofer, R. Grimm, H. von Busch and D. Boll, "A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate", Investigative Radiology, 2021;Publish Ahead of Print.
    Abstract DOI
  34. J. Bleker, D. Yakar, B. van Noort, D. Rouw, I. de Jong, R. Dierckx, T. Kwee and H. Huisman, "Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer", Insights into Imaging, 2021;12(1).
    Abstract DOI
  35. B. Liefers, P. Taylor, A. Alsaedi, C. Bailey, K. Balaskas, N. Dhingra, C. Egan, F. Rodrigues, C. González-Gonzalo, T. Heeren, A. Lotery, P. Muller, A. Olvera-Barrios, B. Paul, R. Schwartz, D. Thomas, A. Warwick, A. Tufail and C. Sánchez, "Quantification of key retinal features in early and late age-related macular degeneration using deep learning", American Journal of Ophthalmology, 2021;226:1-12.
    Abstract DOI
  36. C. Jacobs, A. Setio, E. Scholten, P. Gerke, H. Bhattacharya, F. M. Hoesein, M. Brink, E. Ranschaert, P. de Jong, M. Silva, B. Geurts, K. Chung, S. Schalekamp, J. Meersschaert, A. Devaraj, P. Pinsky, S. Lam, B. van Ginneken and K. Farahani, "Deep Learning for Lung Cancer Detection in Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists", Radiology: Artificial Intelligence, 2021;3(6):e210027.
    Abstract DOI
  37. A. Saha, M. Hosseinzadeh and H. Huisman, "End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction", Medical Image Analysis, 2021:102155.
    Abstract DOI GitHub Algorithm
  38. C. de Vente, L. Boulogne, K. Venkadesh, C. Sital, N. Lessmann, C. Jacobs, C. Sánchez and B. van Ginneken, "Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison", IEEE Transactions on Artificial Intelligence, 2021.
    Abstract DOI
  39. A. Sekuboyina, M. Husseini, A. Bayat, M. Löffler, H. Liebl, H. Li, G. Tetteh, J. Kukačka, C. Payer, D. Štern, 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. Huỳnh, 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", Medical Image Analysis, 2021;73:102166.
    Abstract DOI Cited by ~2
  40. A. Hering, S. Häger, J. Moltz, N. Lessmann, S. Heldmann and B. van Ginneken, "CNN-based Lung CT Registration with Multiple Anatomical Constraints", Medical Image Analysis, 2021;72:102139.
    Abstract DOI Algorithm
  41. K. van Leeuwen, M. de Rooij, S. Schalekamp, B. van Ginneken and M. Rutten, "How does artificial intelligence in radiology improve efficiency and health outcomes?", Pediatric Radiology, 2021.
    Abstract DOI
  42. G. Bortsova, C. González-Gonzalo, S. Wetstein, F. Dubost, I. Katramados, L. Hogeweg, B. Liefers, B. van Ginneken, J. Pluim, M. Veta, C. Sánchez and M. de Bruijne, "Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors", Medical Image Analysis, 2021.
    Abstract DOI arXiv

Papers in conference proceedings

  1. G. Smit, F. Ciompi, M. Cigéhn, A. Bodén, J. van der Laak and C. Mercan, "Quality control of whole-slide images through multi-class semantic segmentation of artifacts", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  2. A. Saha, J. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI —Should Different Clinical Objectives Mandate Different Loss Functions?", Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
    Abstract arXiv
  3. W. Xie, C. Jacobs and B. van Ginneken, "Deep Clustering Activation Maps for Emphysema Subtyping", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  4. D. Geijs, H. Pinckaers, A. Amir and G. Litjens, "End-to-end classification on basal-cell carcinoma histopathology whole-slides images", Medical Imaging, 2021;11603:1160307.
    Abstract DOI
  5. J. Vermazeren, L. van Eekelen, L. Meesters, M. Looijen-Salamon, S. Vos, E. Munari, C. Mercan and F. Ciompi, "muPEN: Multi-class PseudoEdgeNet for PD-L1 assessment", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  6. K. Faryna, J. van der Laak and G. Litjens, "Tailoring automated data augmentation to H&E-stained histopathology", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  7. A. Hering, F. Peisen, T. Amaral, S. Gatidis, T. Eigentler, A. Othman and J. Moltz, "Whole-Body Soft-Tissue Lesion Tracking and Segmentation in Longitudinal CT Imaging Studies", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  8. B. de Wilde, R. ten Broek and H. Huisman, "Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning", Medical Imaging with Deep Learning, 2021.
    Abstract arXiv
  9. M. van Rijthoven, M. Balkenhol, M. Atzori, P. Bult, J. van der Laak and F. Ciompi, "Few-shot weakly supervised detection and retrieval in histopathology whole-slide images", Medical Imaging, 2021;11603:137 - 143.
    Abstract DOI
  10. W. Aswolinskiy, D. Tellez, G. Raya, L. van der Woude, M. Looijen-Salamon, J. van der Laak, K. Grunberg and F. Ciompi, "Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images", Medical Imaging 2021: Digital Pathology, 2021;11603:1 - 7.
    Abstract DOI
  11. S. Häger, S. Heldmann, A. Hering, S. Kuckertz and A. Lange, "Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge", Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020, 2021;12587:74-79.
    Abstract DOI

Abstracts

  1. C. González-Gonzalo, E. Thee, B. Liefers, C. de Vente, C. Klaver and C. Sánchez, "Hierarchical curriculum learning for robust automated detection of low-prevalence retinal disease features: application to reticular pseudodrusen", Association for Research in Vision and Ophthalmology, 2021.
    Abstract
  2. A. Saha, J. Bosma, C. Roest, M. Hosseinzadeh, J. Fütterer and H. Huisman, "Deep Learning with Bayesian Inference for Prostate Cancer Diagnosis across Longitudinal Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract
  3. W. Hendrix, N. Hendrix, M. Prokop, E. Scholten, B. Van Ginneken, M. Rutten and C. Jacobs, "Trends in the Incidence of Pulmonary Nodules in Chest Computed Tomography: 10-Year Results from Two Dutch Hospitals", European Congress of Radiology, 2021.
    Abstract DOI
  4. J. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI", Annual Meeting of the Radiological Society of North America, 2021.
    Abstract

PhD theses

  1. A. Schreuder, "Lung cancer screening: use the scan to decide who to scan when", 2021.
    Abstract Url
  2. D. Tellez, "Advancing computational pathology with deep learning: from patches to gigapixel image-level classification", 2021.
    Abstract Url