Bernhard Kainz

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Daniel RueckertTechnical University of Munich and Imperial College LondonEmail confirmado em tum.de
Ben GlockerImperial College LondonEmail confirmado em imperial.ac.uk
Ozan Oktaylmperial College LondonEmail confirmado em ic.ac.uk
Jo HajnalProfessor of Imaging Science, Kings College LondonEmail confirmado em kcl.ac.uk
Matthew Chung Hai LeeImperial College LondonEmail confirmado em imperial.ac.uk
Steven McDonaghSenior Lecturer, University of EdinburghEmail confirmado em ed.ac.uk
mary rutherfordKings College LondonEmail confirmado em kcl.ac.uk
Amir AlansaryResearch Associate, Biomedical Image Analysis Group (BioMedIA), Imperial College LondonEmail confirmado em imperial.ac.uk
Dieter SchmalstiegAlexander von Humboldt Professor of Visual Computing, University of StuttgartEmail confirmado em visus.uni-stuttgart.de
Konstantinos KamnitsasAssociate Professor of Biomedical Imaging @ University of OxfordEmail confirmado em eng.ox.ac.uk
Martin RajchlResearch Fellow, Dept. of Computing, Imperial College LondonEmail confirmado em imperial.ac.uk
Markus SteinbergerFull Professor, Graz University of Technology, AustriaEmail confirmado em tugraz.at
Maria Kuklisova Murgasova DeprezKing's College LondonEmail confirmado em kcl.ac.uk
Christina MalamateniouAssociate Professor in RadiographyEmail confirmado em city.ac.uk
Kevin KeraudrenApplied Scientist at OnfidoEmail confirmado em onfido.com
Christian F. BaumgartnerUniversity of Tübingen & University of LucerneEmail confirmado em uni-tuebingen.de
Stefan HauswiesnerResearch and Teaching Assistant, Graz University of TechnologyEmail confirmado em icg.tugraz.at
Philip VoglreiterPostDoc Graz University of TechnologyEmail confirmado em icg.tugraz.at
Michael KenzelGerman Research Center for Artificial IntelligenceEmail confirmado em icg.tugraz.at
Denis KalkofenAssociate Professor Graz University of TechnologyEmail confirmado em icg.tugraz.at

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Bernhard Kainz

Imperial College London, FAU Erlangen-Nürnberg

Email confirmado em imperial.ac.uk - Página inicial

human-in-the-loop computing machine learning medical image analysis


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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	7520	2018
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	2311	2018
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	1752	2019
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	843	2017
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	751	2018
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	603	2021
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	601	2018
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	489	2016
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	481	2018
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	411	2017
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	321*	2024
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	281	1804
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	272	2008
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	229	2021
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	216	2021
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	211	2015
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	203	2012
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	202	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	174	2022
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	154	2019

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