| Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge J Ma, Y Zhang, S Gu, C Ge, S Ma, A Young, C Zhu, K Meng, X Yang, ... arXiv preprint arXiv:2308.05862, 2023 | 73 | 2023 |
| Fully automatic deep learning framework for pancreatic ductal adenocarcinoma detection on computed tomography N Alves, M Schuurmans, G Litjens, JS Bosma, J Hermans, H Huisman Cancers 14 (2), 376, 2022 | 45 | 2022 |
| Prediction variability to identify reduced AI performance in cancer diagnosis at MRI and CT N Alves, JS Bosma, KV Venkadesh, C Jacobs, Z Saghir, M de Rooij, ... Radiology 308 (3), e230275, 2023 | 10 | 2023 |
| Artificial intelligence in pancreatic ductal adenocarcinoma imaging: A commentary on potential future applications M Schuurmans, N Alves, P Vendittelli, H Huisman, J Hermans, G Litjens, ... Gastroenterology 165 (2), 309-316, 2023 | 10 | 2023 |
| Setting the research agenda for clinical artificial intelligence in pancreatic adenocarcinoma imaging M Schuurmans, N Alves, P Vendittelli, H Huisman, J Hermans Cancers 14 (14), 3498, 2022 | 7 | 2022 |
| Assessing the need for adaptive radiotherapy in head and neck cancer patients using an automatic planning tool N Alves, JM Dias, H Rocha, T Ventura, J Mateus, M Capela, L Khouri, ... reports of practical Oncology and radiotherapy 26 (3), 423-432, 2021 | 7 | 2021 |
| Predicting the need for adaptive radiotherapy in head and neck patients from CT-Based radiomics and pre-treatment data N Alves, J Dias, T Ventura, J Mateus, M Capela, L Khouri, ... Computational Science and Its Applications–ICCSA 2021: 21st International …, 2021 | 4 | 2021 |
| Do a priori expectations of plan quality offset planning variability in head and neck IMRT? N Alves, T Ventura, J Mateus, M Capela, MC Lopes Radiation Physics and Chemistry 168, 108580, 2020 | 3 | 2020 |
| Uncertainty-Guided Self-learning Framework for Semi-supervised Multi-organ Segmentation N Alves, B De Wilde MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ …, 2022 | 2 | 2022 |
| Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma T Perik, N Alves, JJ Hermans, H Huisman Cancers 16 (3), 577, 2024 | 1 | 2024 |
| Reproducibility of Training Deep Learning Models for Medical Image Analysis J Bosma, D Peeters, A Saha, Z Saghir, C Jacobs, H Huisman Medical Imaging with Deep Learning, 1269-1287, 2024 | 1 | 2024 |
| Unveiling Disease Progression in Chest Radiographs through AI N Alves, KV Venkadesh Radiology: Artificial Intelligence 6 (5), e240426, 2024 | | 2024 |
| Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT Y Yin, RJ de Haas, N Alves, JP Pennings, SJS Ruiter, TC Kwee, D Yakar Abdominal Radiology 49 (4), 1122-1131, 2024 | | 2024 |
| Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation D Peeters, N Alves, KV Venkadesh, R Dinnessen, Z Saghir, ET Scholten, ... European Radiology, 1-13, 2024 | | 2024 |
| The PANORAMA Study Protocol: Pancreatic Cancer Diagnosis - Radiologists Meet AI H Alves, N., Schuurmans, M., Rutkowski, D., Yakar, D., Haldorsen, I ... Zenodo, 2024 | | 2024 |
| Erratum for: Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT N Alves, JS Bosma, KV Venkadesh, C Jacobs, Z Saghir, M de Rooij, ... Radiology 309 (1), e239023, 2023 | | 2023 |
Henkjan HuismanProfessor Medical Imaging AI, Radboud University Medical Centre Nijmegen and NTNU Norway在 radboudumc.nl 的电子邮件经过验证
