| Evaluating reinforcement learning agents for anatomical landmark detection A Alansary, O Oktay, Y Li, L Le Folgoc, B Hou, G Vaillant, K Kamnitsas, ... Medical image analysis 53, 156-164, 2019 | 205 | 2019 |
| Natural synthetic anomalies for self-supervised anomaly detection and localization HM Schlüter, J Tan, B Hou, B Kainz European Conference on Computer Vision, 474-489, 2022 | 166 | 2022 |
| Geomstats: a Python package for Riemannian geometry in machine learning N Miolane, N Guigui, A Le Brigant, J Mathe, B Hou, Y Thanwerdas, ... Journal of Machine Learning Research 21 (223), 1-9, 2020 | 108 | 2020 |
| 3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images B Hou, B Khanal, A Alansary, S McDonagh, A Davidson, M Rutherford, ... IEEE Transactions on Medical Imaging, 2018 | 98 | 2018 |
| Detecting outliers with poisson image interpolation J Tan, B Hou, T Day, J Simpson, D Rueckert, B Kainz Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 74 | 2021 |
| Ratchet: Medical transformer for chest x-ray diagnosis and reporting B Hou, G Kaissis, RM Summers, B Kainz Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 73 | 2021 |
| Standard plane detection in 3d fetal ultrasound using an iterative transformation network Y Li, B Khanal, B Hou, A Alansary, JJ Cerrolaza, M Sinclair, J Matthew, ... International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 70 | 2018 |
| Automatic view planning with multi-scale deep reinforcement learning agents A Alansary, LL Folgoc, G Vaillant, O Oktay, Y Li, W Bai, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 68 | 2018 |
| Predicting slice-to-volume transformation in presence of arbitrary subject motion B Hou, A Alansary, S McDonagh, A Davidson, M Rutherford, JV Hajnal, ... Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 68 | 2017 |
| Detecting outliers with foreign patch interpolation J Tan, B Hou, J Batten, H Qiu, B Kainz arXiv preprint arXiv:2011.04197, 2020 | 65 | 2020 |
| Ultrasound video transformers for cardiac ejection fraction estimation H Reynaud, A Vlontzos, B Hou, A Beqiri, P Leeson, B Kainz Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 62 | 2021 |
| Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging Q Meng, M Sinclair, V Zimmer, B Hou, M Rajchl, N Toussaint, O Oktay, ... IEEE transactions on medical imaging 38 (12), 2755-2767, 2019 | 56 | 2019 |
| Context-sensitive super-resolution for fast fetal magnetic resonance imaging S McDonagh, B Hou, A Alansary, O Oktay, K Kamnitsas, M Rutherford, ... Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and …, 2017 | 56 | 2017 |
| Feasibility of using the privacy-preserving large language model Vicuna for labeling radiology reports P Mukherjee, B Hou, RB Lanfredi, RM Summers Radiology 309 (1), e231147, 2023 | 55 | 2023 |
| Mood 2020: A public benchmark for out-of-distribution detection and localization on medical images D Zimmerer, PM Full, F Isensee, P Jäger, T Adler, J Petersen, G Köhler, ... IEEE Transactions on Medical Imaging 41 (10), 2728-2738, 2022 | 49 | 2022 |
| Self-supervised out-of-distribution detection and localization with natural synthetic anomalies (nsa) HM Schlüter, J Tan, B Hou, B Kainz arXiv preprint arXiv:2109.15222 2 (5), 2021 | 44 | 2021 |
| Computing CNN loss and gradients for pose estimation with Riemannian geometry B Hou, N Miolane, B Khanal, MCH Lee, A Alansary, S McDonagh, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 38 | 2018 |
| Deep learning for cardiac motion estimation: supervised vs. unsupervised training H Qiu, C Qin, L Le Folgoc, B Hou, J Schlemper, D Rueckert Statistical Atlases and Computational Models of the Heart. Multi-Sequence …, 2020 | 18 | 2020 |
| Automatic shadow detection in 2d ultrasound images Q Meng, C Baumgartner, M Sinclair, J Housden, M Rajchl, A Gomez, ... Data Driven Treatment Response Assessment and Preterm, Perinatal, and …, 2018 | 16 | 2018 |
| Introduction to geometric learning in python with geomstats N Miolane, N Guigui, H Zaatiti, C Shewmake, H Hajri, D Brooks, ... SciPy 2020-19th Python in Science Conference, 48-57, 2020 | 14 | 2020 |
Bernhard KainzImperial College London, FAU Erlangen-Nürnbergimperial.ac.uk의 이메일 확인됨