| Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography HA Kirişli, M Schaap, CT Metz, AS Dharampal, WB Meijboom, ... Medical image analysis 17 (8), 859-876, 2013 | 207 | 2013 |
| Electricity price forecasting using recurrent neural networks U Ugurlu, I Oksuz, O Tas Energies 11 (5), 1255, 2018 | 200 | 2018 |
| Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study RD Rudyanto, S Kerkstra, EM Van Rikxoort, C Fetita, PY Brillet, C Lefevre, ... Medical image analysis 18 (7), 1217-1232, 2014 | 151 | 2014 |
| A topological loss function for deep-learning based image segmentation using persistent homology JR Clough, N Byrne, I Oksuz, VA Zimmer, JA Schnabel, AP King IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 8766 …, 2020 | 129 | 2020 |
| Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function B Ruijsink, E Puyol-Antón, I Oksuz, M Sinclair, W Bai, JA Schnabel, ... Cardiovascular Imaging 13 (3), 684-695, 2020 | 121 | 2020 |
| An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy S Ali, F Zhou, B Braden, A Bailey, S Yang, G Cheng, P Zhang, X Li, ... Scientific reports 10 (1), 2748, 2020 | 97 | 2020 |
| Left-ventricle quantification using residual U-Net E Kerfoot, J Clough, I Oksuz, J Lee, AP King, JA Schnabel Statistical Atlases and Computational Models of the Heart. Atrial …, 2019 | 82 | 2019 |
| Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning I Oksuz, B Ruijsink, E Puyol-Anton, J Clough, G Cruz, A Bustin, C Prieto, ... arXiv preprint arXiv:1810.12185, 2018 | 69 | 2018 |
| Statistical shape modeling of the left ventricle: myocardial infarct classification challenge A Suinesiaputra, P Ablin, X Alba, M Alessandrini, J Allen, W Bai, S Cimen, ... IEEE journal of biomedical and health informatics 22 (2), 503-515, 2017 | 69 | 2017 |
| Explicit topological priors for deep-learning based image segmentation using persistent homology JR Clough, I Oksuz, N Byrne, JA Schnabel, AP King Information Processing in Medical Imaging: 26th International Conference …, 2019 | 60 | 2019 |
| Global and local interpretability for cardiac MRI classification JR Clough, I Oksuz, E Puyol-Antón, B Ruijsink, AP King, JA Schnabel Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 42 | 2019 |
| Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation I Oksuz, JR Clough, B Ruijsink, EP Anton, A Bustin, G Cruz, C Prieto, ... IEEE Transactions on Medical Imaging 39 (12), 4001-4010, 2020 | 41 | 2020 |
| Neural network based model comparison for intraday electricity price forecasting I Oksuz, U Ugurlu Energies 12 (23), 4557, 2019 | 36 | 2019 |
| Magnetic resonance fingerprinting using recurrent neural networks I Oksuz, G Cruz, J Clough, A Bustin, N Fuin, RM Botnar, C Prieto, AP King, ... 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 34 | 2019 |
| A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography N Fuin, A Bustin, T Küstner, I Oksuz, J Clough, AP King, JA Schnabel, ... Magnetic resonance imaging 70, 155-167, 2020 | 31 | 2020 |
| Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space I Oksuz, J Clough, B Ruijsink, E Puyol-Antón, A Bustin, G Cruz, C Prieto, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 30 | 2019 |
| Cardiac MR motion artefact correction from k-space using deep learning-based reconstruction I Oksuz, J Clough, A Bustin, G Cruz, C Prieto, R Botnar, D Rueckert, ... Machine Learning for Medical Image Reconstruction: First International …, 2018 | 27 | 2018 |
| A hybrid method for coronary artery stenoses detection and quantification in CTA images İ Öksüz, D Ünay, K Kadıpaşaoğlu MICCAI Workshop 3d Cardiovascular Imaging: A MICCAI Segmentation, 2012 | 27 | 2012 |
| dAUTOMAP: Decomposing AUTOMAP to achieve scalability and enhance performance J Schlemper, I Oksuz, JR Clough, J Duan, AP King, JA Schnabel, ... arXiv preprint arXiv:1909.10995, 2019 | 25 | 2019 |
| Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection I Oksuz, B Ruijsink, E Puyol-Antón, A Bustin, G Cruz, C Prieto, D Rueckert, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 23 | 2018 |
Andrew P KingReader in Medical Image Analysis, King's College LondonVerified email at kcl.ac.uk
