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Bernhard Kainz
Bernhard Kainz
Imperial College London, FAU Erlangen-Nürnberg
Подтвержден адрес электронной почты в домене imperial.ac.uk - Главная страница
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Attention u-net: Learning where to look for the pancreas
O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ...
arXiv preprint arXiv:1804.03999, 2018
70862018
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
arXiv preprint arXiv:1811.02629, 2018
21402018
Attention gated networks: Learning to leverage salient regions in medical images
J Schlemper, O Oktay, M Schaap, M Heinrich, B Kainz, B Glocker, ...
Medical image analysis 53, 197-207, 2019
17012019
Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation
O Oktay, E Ferrante, K Kamnitsas, M Heinrich, W Bai, J Caballero, ...
IEEE transactions on medical imaging 37 (2), 384-395, 2017
8432017
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
W Bai, M Sinclair, G Tarroni, O Oktay, M Rajchl, G Vaillant, AM Lee, ...
Journal of cardiovascular magnetic resonance 20 (1), 65, 2018
7342018
Ensembles of multiple models and architectures for robust brain tumour segmentation
K Kamnitsas, W Bai, E Ferrante, S McDonagh, M Sinclair, N Pawlowski, ...
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018
5922018
A survey on active learning and human-in-the-loop deep learning for medical image analysis
S Budd, EC Robinson, B Kainz
Medical image analysis 71, 102062, 2021
5832021
Deepcut: Object segmentation from bounding box annotations using convolutional neural networks
M Rajchl, MCH Lee, O Oktay, K Kamnitsas, J Passerat-Palmbach, W Bai, ...
IEEE transactions on medical imaging 36 (2), 674-683, 2016
4862016
Attention u-net: Learning where to look for the pancreas. arXiv
O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ...
arXiv preprint arXiv:1804.03999 10, 2018
4292018
SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound
CF Baumgartner, K Kamnitsas, J Matthew, TP Fletcher, S Smith, LM Koch, ...
IEEE transactions on medical imaging 36 (11), 2204-2215, 2017
3832017
Metrics reloaded: recommendations for image analysis validation
L Maier-Hein, A Reinke, P Godau, MD Tizabi, F Buettner, E Christodoulou, ...
Nature methods 21 (2), 195-212, 2024
279*2024
Magnetic resonance–derived 3-dimensional blood flow patterns in the main pulmonary artery as a marker of pulmonary hypertension and a measure of elevated mean pulmonary …
G Reiter, U Reiter, G Kovacs, B Kainz, K Schmidt, R Maier, H Olschewski, ...
Circulation: Cardiovascular Imaging 1 (1), 23-30, 2008
2702008
Attention u-net: learning where to look for the pancreas (2018)
O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ...
arXiv preprint arXiv:1804.03999, 1804
2601804
Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
Q Dou, TY So, M Jiang, Q Liu, V Vardhanabhuti, G Kaissis, Z Li, W Si, ...
NPJ digital medicine 4 (1), 60, 2021
2182021
Fast volume reconstruction from motion corrupted stacks of 2D slices
B Kainz, M Steinberger, W Wein, M Kuklisova-Murgasova, ...
IEEE transactions on medical imaging 34 (9), 1901-1913, 2015
2092015
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
2042019
Intra‐and interobserver variability in fetal ultrasound measurements
I Sarris, C Ioannou, P Chamberlain, E Ohuma, F Roseman, L Hoch, ...
Ultrasound in obstetrics & gynecology 39 (3), 266-273, 2012
2002012
Common limitations of image processing metrics: A picture story
A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ...
arXiv preprint arXiv:2104.05642, 2021
1982021
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
1572022
Three-dimensional visualisation of the fetal heart using prenatal MRI with motion-corrected slice-volume registration: a prospective, single-centre cohort study
DFA Lloyd, K Pushparajah, JM Simpson, JFP Van Amerom, ...
The Lancet 393 (10181), 1619-1627, 2019
1522019
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