B. Turkbey, H. Huisman, A. Fedorov, K. Macura, D. Margolis, V. Panebianco, A. Oto, I. Schoots, M. Siddiqui, C. Moore, O. Rouvière, L. Bittencourt, A. Padhani, C. Tempany and M. Haider, "Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0",
Radiology,
2025;315.
A. Farris, J. van der Laak and D. van Midden, "Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection",
Current Opinion in Organ Transplantation,
2025.
W. Hendrix, N. Hendrix, E. Scholten, B. van Ginneken, M. Prokop, M. Rutten and C. Jacobs, "Artificial intelligence for the detection of airway nodules in chest CT scans",
European Radiology,
2025.
C. Roest, T. Kwee, I. de Jong, I. Schoots, P. van Leeuwen, S. Heijmink, H. van der Poel, S. Fransen, A. Saha, H. Huisman and D. Yakar, "Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression",
Radiology: Imaging Cancer,
2025;7.
L. Thijssen, M. de Rooij and H. Huisman, "External validation of automated prostate MR T2-weighted image quality assessment on multi-centre multi-vendor data",
European Journal of Radiology Artificial Intelligence,
2025;1:100002.
H. Long, L. Hu, L. Wei, L. Dai, C. Fu, Y. Lu, C. Xu, Z. Hu, L. Wang, Z. Xu, R. Grimm, H. Busch, T. Benkert, A. Kamen, B. Lou, H. Huisman, A. Tong, T. Penzkofer, M. Choi, I. Shabunin, D. Winkel, P. Xing, D. Szolar, F. Coakley, S. Shea, E. Szurowska, W. Zhu and J. Zhao, "Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy",
Quantitative Imaging in Medicine and Surgery,
2025;15:2132-2145.
Q. Lohuizen, S. Fransen, G. Yiasemis, C. Roest, F. Simonis, T. Kwee, D. Yakar and H. Huisman, "Deep learning reconstruction for prostate MRI: Impact of field-of-view selection on global and regional image quality",
European Journal of Radiology Artificial Intelligence,
2025;2:100017.
M. van Rijthoven, W. Aswolinskiy, L. Tessier, M. Balkenhol, J. Bogaerts, D. Drubay, L. Blesa, D. Peeters, E. Stovgaard, A. L\aenkholm , H. Haynes, L. Craciun, D. Larsimont, M. Amgad, L. Cooper, C. de Kock, V. Dechering, J. Lotz, N. Weiss, M. van Bockstal, C. Galant, E. Lips, H. Horlings, J. Wesseling, L. Mulder, S. van den Belt, K. Weber, P. Jank, C. Denkert, E. Munari, G. Bogina, C. Russ, A. Lemm, S. Loi, J. Douglas, S. Michiels, H. Joensuu, M. Fan, D. Lee, J. Ye, K. Byun, J. Kim, S. Xu, Z. Ji, F. Xie, J. Kuang, X. Chen, L. Chen, A. Tsakiroglou, R. Byers, M. Fergie, V. Ramanathan, A. Martel, A. Shephard, S. Ahmed Raza, M. Jahanifar, N. Rajpoot, S. Cho, D. Kim, H. Jang, C. Park, K. Kim, R. Donders, S. Maurits, M. Groeneveld, A. Mickan, J. Meakin, B. van Ginneken, R. Salgado, J. van der Laak and F. Ciompi, "Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge",
Preprint,
2025.
D. Peeters, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection",
Computers in Biology and Medicine,
2025;186:109633.
R. van der Waerden, R. Volleberg, T. Luttikholt, P. Cancian, J. van der Zande, G. Stone, N. Holm, E. Kedhi, J. Escaned, D. Pellegrini, G. Guagliumi, S. Mehta, N. Pinilla-Echeverri, R. Moreno, L. Räber, T. Roleder, B. van Ginneken, C. Sánchez, I. Isgum, N. van Royen and J. Thannhauser, "Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review",
European Heart Journal - Digital Health,
2025.
A. Jiménez-Sánchez, N. Avlona, S. de Boer, V. Campello, A. Feragen, E. Ferrante, M. Ganz, J. Gichoya, C. González, S. Groefsema, A. Hering, A. Hulman, L. Joskowicz, D. Juodelyte, M. Kandemir, T. Kooi, J. Lérida, L. Li, A. Pacheco, T. Rädsch, M. Reyes, T. Sourget, B. van Ginneken, D. Wen, N. Weng, J. Xu, H. Zając, M. Zuluaga and V. Cheplygina, "In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review",
arXiv:2501.10727,
2025.
P. Cancian, S. Saitta, X. Gu, R. van Herten, T. Luttikholt, J. Thannhauser, R. Volleberg, R. van der Waerden, J. van der Zande, C. Sánchez, B. van Ginneken, N. van Royen and I. Isgum, "Attenuation artifact detection and severity classification in intracoronary OCT using mixed image representations",
arXiv:2503.05322,
2025.
S. Sun, D. van Midden, G. Litjens and C. Baumgartner, "Prototype-Based Multiple Instance Learning for Gigapixel Whole Slide Image Classification",
arXiv:2503.08384,
2025.
Y. Wang, T. Chen, S. Vinayahalingam, T. Wu, C. Chang, H. Chang, H. Wei, M. Chen, C. Ko, D. Moin, B. van Ginneken, T. Xi, H. Tsai, M. Chen, T. Hsu and H. Chou, "Artificial Intelligence to Assess Dental Findings from Panoramic Radiographs -- A Multinational Study",
arXiv:2502.10277,
2025.
W. Heyer, Y. Elser, L. Berkel, X. Song, X. Xu, P. Yan, X. Jia, J. Duan, Z. Li, T. Mok, B. LI, C. Staackmann, C. Gro\ssbr öhmer, L. Hansen, A. Hering, M. Sieren and M. Heinrich, "OncoReg: Medical Image Registration for Oncological Challenges",
arXiv:2503.23179,
2025.
M. D'Amato, J. van der Laak and F. Ciompi, ""No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images",
arXiv:2502.21109,
2025.
S. Sun, L. Tessier, F. Meeuwsen, C. Grisi, D. van Midden, G. Litjens and C. Baumgartner, "Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology",
arXiv:2501.02922,
2025.
J. Lefkes, C. Grisi and G. Litjens, "A Balancing Act: Optimizing Classification and Retrieval in Cross-Modal Vision Models",
Medical Imaging with Deep Learning,
2025.
C. Lems, N. Klubíčková, B. Brattoli, T. Lee, S. Kim, V. Besler, P. Fernandez, L. Pons, A. Laurinavicius, J. Drachneris, D. Montezuma, D. Oliveira, S. Vos, M. Balkenhol, J. van Ipenburg, A. Vos, M. Poceviciute, N. Khalili and F. Ciompi, "Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study",
Laboratory Investigation,
2025;105:103609.
X. Pham, M. Prokop, B. van Ginneken and A. Hering, "Divide to conquer: a field decomposition approach for multi-organ whole-body CT image registration",
Medical Imaging 2025: Image Processing,
2025:48.
G. Lozupone, A. Bria, F. Fontanella, F. Meijer, C. Stefano and H. Huisman, "Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging",
2025.
J. Lefkes, M. D'Amato, S. Sun, G. Litjens and F. Ciompi, "Large Language Models Automate Diagnostic Conclusions Generation from Microscopic Descriptions in Multiple Cancer Types",
Laboratory Investigation,
2025;105:103608.
N. Antonissen, I. Houben, O. Tryfonos, M. De Rooij and K. Van Leeuwen, "Evolution of commercially available artificial intelligence in radiology: a follow-up on peer-reviewed evidence of 179 products",
European Congress of Radiology,
2025.
D. Peeters, B. Obreja, N. Antonissen, R. Dinnessen, Z. Saghir, E. Scholten, R. Vliegenthart, M. Prokop and C. Jacobs, "Benchmarking of Artificial Intelligence and Radiologists for Lung Cancer Screening in CT: The LUNA25 Challenge",
European Congress of Radiology,
2025.