Publications
2024
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IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent dataInformation FusionT. Zhang, T. Tan, L. Han, X. Wang, Y. Gao, J. Van Dijk, A. Portaluri, A. Gonzalez-Huete, A. D’Angelo, C. Lu, J. Teuwen, R. Beets-Tan, Y. Sun, R. Mann, 2024, 108
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Enhancing Cross-Modal Medical Image Segmentation through CompositionalityA. Eijpe, V. Corbetta, K. Chupetlovska, R. Beets-Tan, W. Silva, 2024
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FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationP. Schutte, V. Corbetta, R. Beets-Tan, W. Silva, 2024
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Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic featuresComputers in Biology and MedicineM. Ahmadian, Z. Bodalal, H. J. Van Der Hulst, C. Vens, L. Karssemakers, N. Bogveradze, F. Castagnoli, F. Landolfi, E. K. Hong, N. Gennaro, A. D. Pizzi, R. G. Beets-Tan, M. W. Van Den Brekel, J. A. Castelijns, 2024
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Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variationEuropean Journal of Radiology OpenT. M. Tareco Bucho, L. Petrychenko, M. A. Abdelatty, N. Bogveradze, Z. Bodalal, R. G. Beets-Tan, S. Trebeschi, 2024, 12
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Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?European Journal of RadiologyD. J. Van Der Reijd, K. Chupetlovska, E. Van Dijk, B. Westerink, M. A. Monraats, J. J. Van Griethuysen, D. M. Lambregts, R. Tissier, R. G. Beets-Tan, S. Benson, M. Maas, 2024, 172
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METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMIIInsights into ImagingB. Kocak, T. Akinci D’Antonoli, N. Mercaldo, A. Alberich-Bayarri, B. Baessler, I. Ambrosini, A. E. Andreychenko, S. Bakas, R. G. H. Beets-Tan, K. Bressem, I. Buvat, R. Cannella, L. A. Cappellini, A. U. Cavallo, L. L. Chepelev, L. C. H. Chu, A. Demircioglu, N. M. Desouza, M. Dietzel, S. C. Fanni, A. Fedorov, L. S. Fournier, V. Giannini, R. Girometti, K. B. W. Groot Lipman, G. Kalarakis, B. S. Kelly, M. E. Klontzas, D. Koh, E. Kotter, H. Y. Lee, M. Maas, L. Marti-Bonmati, H. Müller, N. Obuchowski, F. Orlhac, N. Papanikolaou, E. Petrash, E. Pfaehler, D. Pinto Dos Santos, A. Ponsiglione, S. Sabater, F. Sardanelli, P. Seeböck, N. M. Sijtsema, A. Stanzione, A. Traverso, L. Ugga, M. Vallières, L. V. Van Dijk, J. J. M. Van Griethuysen, R. W. Van Hamersvelt, P. Van Ooijen, F. Vernuccio, A. Wang, S. Williams, J. Witowski, Z. Zhang, A. Zwanenburg, R. Cuocolo, 2024, 15;(1)
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An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre studynpj Precision OncologyL. Cai, D. M. J. Lambregts, G. L. Beets, M. Mass, E. H. P. Pooch, C. Guérendel, R. G. H. Beets-Tan, S. Benson, 2024, 8;(1)
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Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?Journal of Cancer Research and Clinical OncologyM. Yeghaian, T. M. Tareco Bucho, M. De Bruin, A. Schmitz, Z. Bodalal, E. F. Smit, R. G. H. Beets-Tan, D. Van Den Broek, S. Trebeschi, 2024, 150;(6)
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Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysisEuropean RadiologyG. Agrotis, E. Pooch, M. Abdelatty, S. Benson, A. Vassiou, M. Vlychou, R. G. H. Beets-Tan, I. G. Schoots, 2024
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AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging: JMRIR. Lo Gullo, J. Brunekreef, E. Marcus, L. K. Han, S. Eskreis-Winkler, S. B. Thakur, R. Mann, K. Groot Lipman, J. Teuwen, K. Pinker, 2024
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Large retrorectal spindle cell sarcoma: A case report and brief review of the literatureRadiology Case ReportsC. L. Petersen, M. R. Byriel, J. Shkurti, S. R. Rafaelsen, 2024, 19;(7):2684-2688
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2023
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Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention MechanismLecture Notes in Computer ScienceT. Zhang, L. Han, A. D’Angelo, X. Wang, Y. Gao, C. Lu, J. Teuwen, R. Beets-Tan, T. Tan, R. Mann, 2023
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Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy DataV. Corbetta, R. Beets-Tan, W. Silva, 2023
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Attention-Based Regularisation for Improved Generalisability in Medical Multi-Centre Data2023 International Conference on Machine Learning and Applications (ICMLA)D. Silva, G. Agrotis, R. Beets-Tan, L. F. Teixeira, W. Silva, 2023
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How to 19F MRI: applications, technique, and getting startedBJR|OpenO. Maxouri, Z. Bodalal, M. Daal, S. Rostami, I. Rodriguez, L. Akkari, M. Srinivas, R. Bernards, R. Beets-Tan, 2023, 5;(1)
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RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast diseaseCell Reports MedicineT. Zhang, T. Tan, X. Wang, Y. Gao, L. Han, L. Balkenende, A. D’Angelo, L. Bao, H. M. Horlings, J. Teuwen, R. G. Beets-Tan, R. M. Mann, 2023
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Artificial Intelligence–based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed PatientsJournal of Thoracic ImagingK. B. Groot Lipman, T. N. Boellaard, C. J. De Gooijer, N. Bogveradze, E. K. Hong, F. Landolfi, F. Castagnoli, N. Vakhidova, I. Smesseim, F. Van Der Heijden, R. G. Beets-Tan, R. Wittenberg, Z. Bodalal, J. A. Burgers, S. Trebeschi, 2023
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How Does Target Lesion Selection Affect RECIST? A Computer Simulation StudyInvestigative RadiologyT. M. Tareco Bucho, R. L. Tissier, K. B. Groot Lipman, Z. Bodalal, A. Delli Pizzi, T. D. L. Nguyen-Kim, R. G. Beets-Tan, S. Trebeschi, 2023
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Diagnostic accuracy of CT for local staging of colon cancer: A nationwide study in the NetherlandsEuropean Journal of CancerJ. Shkurti, K. Van Den Berg, F. N. Van Erning, M. J. Lahaye, R. G. Beets-Tan, J. Nederend, 2023, 193
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Predicting breast cancer types on and beyond molecular level in a multi-modal fashionnpj Breast CancerT. Zhang, T. Tan, L. Han, L. Appelman, J. Veltman, R. Wessels, K. M. Duvivier, C. Loo, Y. Gao, X. Wang, H. M. Horlings, R. G. H. Beets-Tan, R. M. Mann, 2023, 9;(1)
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Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI)European RadiologyL. Topff, K. B. W. Groot Lipman, F. Guffens, R. Wittenberg, A. Bartels-Rutten, G. Van Veenendaal, M. Hess, K. Lamerigts, J. Wakkie, E. Ranschaert, S. Trebeschi, J. J. Visser, R. G. H. Beets-Tan, J. Guiot, A. Snoeckx, P. Kint, L. Van Hoe, C. C. Quattrocchi, D. Dieckens, S. Lounis, E. Schulze, A. E. Sjer, N. Van Vucht, J. A. Tielbeek, F. Raat, D. Eijspaart, A. Abbas, 2023, 33;(6):4249-4258
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Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastasesInsights into ImagingZ. Bodalal, N. Bogveradze, L. C. Ter Beek, J. G. Van Den Berg, J. Sanders, I. Hofland, S. Trebeschi, K. B. W. Groot Lipman, K. Storck, E. K. Hong, N. Lebedyeva, M. Maas, R. G. H. Beets-Tan, F. M. Gomez, I. Kurilova, 2023, 14;(1)
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Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablationEuropean RadiologyD. J. Van Der Reijd, C. Guerendel, F. C. R. Staal, M. P. Busard, M. De Oliveira Taveira, E. G. Klompenhouwer, K. F. D. Kuhlmann, A. Moelker, C. Verhoef, M. P. A. Starmans, D. M. J. Lambregts, R. G. H. Beets-Tan, S. Benson, M. Maas, 2023
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A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph NodesDiagnosticsT. C. Sluckin, M. Hekhuis, S. Q. Kol, J. Nederend, K. Horsthuis, R. G. H. Beets-Tan, G. L. Beets, J. W. A. Burger, J. B. Tuynman, H. J. T. Rutten, M. Kusters, S. Benson, 2023, 13;(19):3099
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Sense and non-sense of imaging in the era of organ preservation for rectal cancerThe British Journal of RadiologyX. Ou, D. J. Van Der Reijd, D. M. Lambregts, B. A. Grotenhuis, B. Van Triest, G. L. Beets, R. G. Beets-Tan, M. Maas, 2023, 96;(1151)
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The diagnostic accuracy of local staging in colon cancer based on computed tomography (CT): evaluating the role of extramural venous invasion and tumour depositsAbdominal RadiologyK. Van Den Berg, S. Wang, J. M. W. E. Willems, G. J. Creemers, J. M. L. Roodhart, J. Shkurti, J. W. A. Burger, H. J. T. Rutten, R. G. H. Beets-Tan, J. Nederend, 2023, 49;(2):365-374
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Whole‐body MRI with diffusion‐weighted imaging as an adjunct to18F‐
fluorodeoxyglucose positron emission tomography andCT in patients with suspected recurrent colorectal cancerColorectal DiseaseJ. R. J. Willemse, M. J. Lahaye, N. F. M. Kok, B. A. Grotenhuis, A. G. J. Aalbers, G. L. Beets, C. Rijsemus, M. Maas, L. W. Van Golen, R. G. H. Beets‐Tan, D. M. J. Lambregts, 2023, 26;(2):290-299Abstract
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CIRSE Position Paper on Artificial Intelligence in Interventional RadiologyCardioVascular and Interventional RadiologyA. Najafi, R. L. Cazzato, B. C. Meyer, P. L. Pereira, A. Alberich, A. López, M. Ronot, J. Fritz, M. Maas, S. Benson, P. Haage, F. Gomez Munoz, 2023, 46;(10):1303-1307
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2022
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Imaging of colorectal nodal diseaseThe Lymphatic System in Colorectal CancerL. Cai, Z. Bodalal, S. Trebeschi, S. Waktola, T. C. Sluckin, M. Kusters, M. Maas, R. Beets-Tan, S. Benson, 2022
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CNN-based tumor progression prediction after thermal ablation with CT imagingMedical Imaging 2022: Computer-Aided DiagnosisM. Taghavi, M. Maas, F. Staal, R. Beets-Tan, S. Benson, 2022
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Federated learning enables big data for rare cancer boundary detection.Nature communicationsS. Pati, U. Baid, B. Edwards, M. Sheller, S. Wang, G. A. Reina, P. Foley, A. Gruzdev, D. Karkada, C. Davatzikos, C. Sako, S. Ghodasara, M. Bilello, S. Mohan, P. Vollmuth, G. Brugnara, C. J. Preetha, F. Sahm, K. Maier-Hein, M. Zenk, M. Bendszus, W. Wick, E. Calabrese, J. Rudie, J. Villanueva-Meyer, S. Cha, M. Ingalhalikar, M. Jadhav, U. Pandey, J. Saini, J. Garrett, M. Larson, R. Jeraj, S. Currie, R. Frood, K. Fatania, R. Y. Huang, K. Chang, C. Balaña, J. Capellades, J. Puig, J. Trenkler, J. Pichler, G. Necker, A. Haunschmidt, S. Meckel, G. Shukla, S. Liem, G. S. Alexander, J. Lombardo, J. D. Palmer, A. E. Flanders, A. P. Dicker, H. I. Sair, C. K. Jones, A. Venkataraman, M. Jiang, T. Y. So, C. Chen, P. A. Heng, Q. Dou, M. Kozubek, F. Lux, J. Michálek, P. Matula, M. Keřkovský, T. Kopřivová, M. Dostál, V. Vybíhal, M. A. Vogelbaum, J. R. Mitchell, J. Farinhas, J. A. Maldjian, C. G. B. Yogananda, M. C. Pinho, D. Reddy, J. Holcomb, B. C. Wagner, B. M. Ellingson, T. F. Cloughesy, C. Raymond, T. Oughourlian, A. Hagiwara, C. Wang, M. To, S. Bhardwaj, C. Chong, M. Agzarian, A. X. Falcão, S. B. Martins, B. C. A. Teixeira, F. Sprenger, D. Menotti, D. R. Lucio, P. Lamontagne, D. Marcus, B. Wiestler, F. Kofler, I. Ezhov, M. Metz, R. Jain, M. Lee, Y. W. Lui, R. Mckinley, J. Slotboom, P. Radojewski, R. Meier, R. Wiest, D. Murcia, E. Fu, R. Haas, J. Thompson, D. R. Ormond, C. Badve, A. E. Sloan, V. Vadmal, K. Waite, R. R. Colen, L. Pei, M. Ak, A. Srinivasan, J. R. Bapuraj, A. Rao, N. Wang, O. Yoshiaki, T. Moritani, S. Turk, J. Lee, S. Prabhudesai, F. Morón, J. Mandel, K. Kamnitsas, B. Glocker, L. V. M. Dixon, M. Williams, P. Zampakis, V. Panagiotopoulos, P. Tsiganos, S. Alexiou, I. Haliassos, E. I. Zacharaki, K. Moustakas, C. Kalogeropoulou, D. M. Kardamakis, Y. S. Choi, S. Lee, J. H. Chang, S. S. Ahn, B. Luo, L. Poisson, N. Wen, P. Tiwari, R. Verma, R. Bareja, I. Yadav, J. Chen, N. Kumar, M. Smits, S. R. Van Der Voort, A. Alafandi, F. Incekara, M. M. J. Wijnenga, G. Kapsas, R. Gahrmann, J. W. Schouten, H. J. Dubbink, A. J. P. E. Vincent, M. J. Van Den Bent, P. J. French, S. Klein, Y. Yuan, S. Sharma, T. Tseng, S. Adabi, S. P. Niclou, O. Keunen, A. Hau, M. Vallières, D. Fortin, M. Lepage, B. Landman, K. Ramadass, K. Xu, S. Chotai, L. B. Chambless, A. Mistry, R. C. Thompson, Y. Gusev, K. Bhuvaneshwar, A. Sayah, C. Bencheqroun, A. Belouali, S. Madhavan, T. C. Booth, A. Chelliah, M. Modat, H. Shuaib, C. Dragos, A. Abayazeed, K. Kolodziej, M. Hill, A. Abbassy, S. Gamal, M. Mekhaimar, M. Qayati, M. Reyes, J. E. Park, J. Yun, H. S. Kim, A. Mahajan, M. Muzi, S. Benson, R. G. H. Beets-Tan, J. Teuwen, A. Herrera-Trujillo, M. Trujillo, W. Escobar, A. Abello, J. Bernal, J. Gómez, J. Choi, S. Baek, Y. Kim, H. Ismael, B. Allen, J. M. Buatti, A. Kotrotsou, H. Li, T. Weiss, M. Weller, A. Bink, B. Pouymayou, H. F. Shaykh, J. Saltz, P. Prasanna, S. Shrestha, K. M. Mani, D. Payne, T. Kurc, E. Pelaez, H. Franco-Maldonado, F. Loayza, S. Quevedo, P. Guevara, E. Torche, C. Mendoza, F. Vera, E. Ríos, E. López, S. A. Velastin, G. Ogbole, M. Soneye, D. Oyekunle, O. Odafe-Oyibotha, B. Osobu, M. Shu'Aibu, A. Dorcas, F. Dako, A. L. Simpson, M. Hamghalam, J. J. Peoples, R. Hu, A. Tran, D. Cutler, F. Y. Moraes, M. A. Boss, J. Gimpel, D. K. Veettil, K. Schmidt, B. Bialecki, S. Marella, C. Price, L. Cimino, C. Apgar, P. Shah, B. Menze, J. S. Barnholtz-Sloan, J. Martin, S. Bakas, 2022, 13;(1):7346
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Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aidEuropean RadiologyK. B. W. Groot Lipman, C. J. De Gooijer, T. N. Boellaard, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Trebeschi, J. A. Burgers, 2022, 33;(5):3557-3565
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The Future of Artificial Intelligence Applied to Immunotherapy TrialsNeoadjuvant Immunotherapy Treatment of Localized Genitourinary CancersZ. Bodalal, S. Trebeschi, I. Wamelink, K. G. Lipman, T. Bucho, N. Van Dijk, T. Boellaard, S. Waktola, R. G. H. Beets-Tan, 2022
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Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiativeEuropean RadiologyA. Ponsiglione, A. Stanzione, G. Spadarella, A. Baran, L. A. Cappellini, K. G. Lipman, P. Van Ooijen, R. Cuocolo, 2022, 33;(3):2239-2247
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Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net ApproachJournal of Computer Assisted TomographyN. A. Coorens, K. G. Lipman, S. P. Krishnam, C. O. Tan, L. Alic, R. Gupta, 2022, 47;(1):93-101
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2021
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An improved automatic system for aiding the detection of colon polyps using deep learning2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)L. Cai, R. Beets-Tan, S. Benson, 2021
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Artificial intelligence-mediated diagnosis of asbestosisILD / DPLD of known originK. B. W. G. Lipman, C. J. De Gooijer, T. N. Boellaard, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Trebeschi, S. Burgers, 2021
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Pleural plaque volume correlation to lung function and artificial intelligence-driven pleural plaque quantificationILD / DPLD of known originK. B. W. G. Lipman, T. N. Boellaard, C. J. De Gooijer, N. Bogveradze, E. K. Hong, F. Landolfi, F. Castagnoli, L. C. Cavallo, N. Lebedyeva, F. Van Der Heijden, R. G. H. Beets-Tan, Z. Bodalal, S. Burgers, S. Trebeschi, 2021
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Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving ImmunotherapyFrontiers in OncologyS. Trebeschi, Z. Bodalal, T. N. Boellaard, T. M. Tareco Bucho, S. G. Drago, I. Kurilova, A. M. Calin-Vainak, A. Delli Pizzi, M. Muller, K. Hummelink, K. J. Hartemink, T. D. L. Nguyen-Kim, E. F. Smit, H. J. W. L. Aerts, R. G. H. Beets-Tan, 2021, 11
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Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving ImmunotherapyFrontiers in OncologyS. Trebeschi, Z. Bodalal, N. Van Dijk, T. N. Boellaard, P. Apfaltrer, T. M. Tareco Bucho, T. D. L. Nguyen-Kim, M. S. Van Der Heijden, H. J. W. L. Aerts, R. G. H. Beets-Tan, 2021, 11
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The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiationSurgical EndoscopyH. E. Haak, X. Gao, M. Maas, S. Waktola, S. Benson, R. G. H. Beets-Tan, G. L. Beets, M. Van Leerdam, J. Melenhorst, 2021, 36;(5):3592-3600
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Novel Breast Specimen Orientation Approach through 3D Visualizations for Relocating Inadequate Margins based on the Surgical Clips: Feasibility Study.R. F. V. Doremalen, K. B. G. Lipman, E. V. '. Riet, H. Torrenga, M. M. Smits, F. V. D. Heijden, 2021
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Creating small but meaningful representations of digital pathology imagesProceedings of the MICCAI Workshop on Computational PathologyC. Guérendel, P. Arnold, B. Torben-Nielsen, 2021
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