Bernhard Kainz

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

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Bernhard Kainz

Imperial College London, FAU Erlangen-Nürnberg

Email verificata su imperial.ac.uk - Home page

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


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Attention U-Net: Learning Where to Look for the Pancreas O Oktay arXiv preprint arXiv:1804.03999, 2018	6099	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	1945	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	1536	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	809	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	689	2018
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	559	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	527	2021
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	468	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	374	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	356	2017
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	264	2008
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	211	1804
Metrics reloaded: Pitfalls and recommendations for image analysis validation L Maier-Hein, B Menze arXiv. org, 2022	200*	2022
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	200	2015
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	195	2021
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	194	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	191	2019
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	170	2021
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	148	2019
Advances in automatic differentiation CH Bischof, HM Bücker, P Hovland, U Naumann, J Utke Springer Berlin Heidelberg, 2008	133	2008

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