| Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists A Rodriguez-Ruiz, K Lĺng, A Gubern-Merida, M Broeders, G Gennaro, ... JNCI: Journal of the National Cancer Institute 111 (9), 916-922, 2019 | 278 | 2019 |
| Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network D Jiang, W Dou, L Vosters, X Xu, Y Sun, T Tan Japanese journal of radiology 36 (9), 566-574, 2018 | 144 | 2018 |
| Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach Y Li, WG Cui, H Huang, YZ Guo, K Li, T Tan Knowledge-Based Systems 164, 96-106, 2019 | 92 | 2019 |
| Computer-aided detection of cancer in automated 3-D breast ultrasound T Tan, B Platel, R Mus, L Tabar, RM Mann, N Karssemeijer IEEE transactions on medical imaging 32 (9), 1698-1706, 2013 | 89 | 2013 |
| Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation T Tan, B Platel, H Huisman, CI Sánchez, R Mus, N Karssemeijer IEEE transactions on medical imaging 31 (5), 1034-1042, 2012 | 87 | 2012 |
| Automated Detection of Cerebral Microbleeds in Patients with Traumatic Brain Injury BP TLA van den Heuvel, AW van der Eerden, R Manniesing, M Ghafoorian, T Tan ... NeuroImage: Clinical 12, 241-251, 2016 | 60 | 2016 |
| Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts JCM van Zelst, T Tan, P Clauser, A Domingo, MD Dorrius, D Drieling, ... European radiology 28 (7), 2996-3006, 2018 | 55 | 2018 |
| Mass segmentation in automated 3-D breast ultrasound using adaptive region growing and supervised edge-based deformable model E Kozegar, M Soryani, H Behnam, M Salamati, T Tan IEEE transactions on medical imaging 37 (4), 918-928, 2017 | 46 | 2017 |
| Improved cancer detection in automated breast ultrasound by radiologists using computer aided detection JCM Van Zelst, T Tan, B Platel, M De Jong, A Steenbakkers, M Mourits, ... European journal of radiology 89, 54-59, 2017 | 45 | 2017 |
| Deep learning approach to semantic segmentation in 3d point cloud intra-oral scans of teeth FG Zanjani, DA Moin, B Verheij, F Claessen, T Cherici, T Tan International Conference on Medical Imaging with Deep Learning, 557-571, 2019 | 40 | 2019 |
| Retinal artery/vein classification using genetic-search feature selection F Huang, B Dashtbozorg, T Tan, BM ter Haar Romeny Computer methods and programs in biomedicine 161, 197-207, 2018 | 40 | 2018 |
| Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning T Tan, Z Li, H Liu, FG Zanjani, Q Ouyang, Y Tang, Z Hu, Q Li IEEE Journal of Translational Engineering in Health and Medicine 6, 2018 | 39 | 2018 |
| Computer‐aided detection of breast cancers using Haar‐like features in automated 3D breast ultrasound T Tan, JJ Mordang, J van Zelst, A Grivegnée, A Gubern‐Mérida, ... Medical physics 42 (4), 1498-1504, 2015 | 35 | 2015 |
| Deep learning methods for lung cancer segmentation in whole-slide histopathology images—the acdc@ lunghp challenge 2019 Z Li, J Zhang, T Tan, X Teng, X Sun, H Zhao, L Liu, Y Xiao, B Lee, Y Li, ... IEEE Journal of Biomedical and Health Informatics 25 (2), 429-440, 2020 | 34 | 2020 |
| Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts E Kozegar, M Soryani, H Behnam, M Salamati, T Tan Ultrasonics 79, 68-80, 2017 | 32 | 2017 |
| Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database X Xu, R Wang, P Lv, B Gao, C Li, Z Tian, T Tan, F Xu Biomedical optics express 9 (7), 3153-3166, 2018 | 31 | 2018 |
| Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound T Tan, B Platel, T Twellmann, G van Schie, R Mus, A Grivegnée, ... Academic radiology 20 (11), 1381-1388, 2013 | 30 | 2013 |
| Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting M Wu, X Zhong, Q Peng, M Xu, S Huang, J Yuan, J Ma, T Tan European journal of radiology 114, 175-184, 2019 | 29 | 2019 |
| Multi-modal and multi-vendor retina image registration Z Li, F Huang, J Zhang, B Dashtbozorg, S Abbasi-Sureshjani, Y Sun, ... Biomedical optics express 9 (2), 410-422, 2018 | 28 | 2018 |
| Lesion segmentation in ultrasound using semi-pixel-wise cycle generative adversarial nets J Xing, Z Li, B Wang, Y Qi, B Yu, FG Zanjani, A Zheng, R Duits, T Tan IEEE/ACM transactions on computational biology and bioinformatics, 2020 | 25 | 2020 |
Nico KarssemeijerProfessor of Computer Aided Diagnosis, Radboud University NijmegenGeverifieerd e-mailadres voor radboudumc.nl
