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Ozan Oktay

Showing results (1-10 of 20) with videos related to

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Medical Image Analysis|February 27, 2019
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutionsMattias P Heinrich, Ozan Oktay, Nassim Bouteldja
International Journal of Computer Assisted Radiology and Surgery|June 1, 2018
TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutionsMattias P Heinrich, Max Blendowski, Ozan Oktay
The Science of the Total Environment|December 18, 2025
Trophic transfer of nanoplastics reduces larval survival of marine fish more than waterborne exposureSiti Syazwani Azmi, Ozan Oktay, Hee-Jin Kim, et al.
Medical Image Analysis|February 26, 2019
Attention gated networks: Learning to leverage salient regions in medical imagesJo Schlemper, Ozan Oktay, Michiel Schaap, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Biomechanically driven registration of pre- to intra-operative 3D images for laparoscopic surgeryOzan Oktay, Li Zhang, Tommaso Mansi, et al.
Frontiers in Pediatrics|December 27, 2016
Regional Differences in End-Diastolic Volumes between 3D Echo and CMR in HLHS PatientsAlberto Gomez, Ozan Oktay, Daniel Rueckert, et al.
Scientific Reports|February 14, 2020
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK BiobankGiacomo Tarroni, Wenjia Bai, Ozan Oktay, et al.
Medical Image Analysis|February 21, 2019
Evaluating reinforcement learning agents for anatomical landmark detectionAmir Alansary, Ozan Oktay, Yuanwei Li, et al.
IEEE Transactions on Medical Imaging|January 6, 2017
Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac ImagesOzan Oktay, Wenjia Bai, Ricardo Guerrero, et al.
IEEE Transactions on Medical Imaging|November 16, 2016
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural NetworksMartin Rajchl, Matthew C H Lee, Ozan Oktay, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
Medical Image Analysis|February 27, 2019
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutionsMattias P Heinrich, Ozan Oktay, Nassim Bouteldja
International Journal of Computer Assisted Radiology and Surgery|June 1, 2018
TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutionsMattias P Heinrich, Max Blendowski, Ozan Oktay
The Science of the Total Environment|December 18, 2025
Trophic transfer of nanoplastics reduces larval survival of marine fish more than waterborne exposureSiti Syazwani Azmi, Ozan Oktay, Hee-Jin Kim, et al.
Medical Image Analysis|February 26, 2019
Attention gated networks: Learning to leverage salient regions in medical imagesJo Schlemper, Ozan Oktay, Michiel Schaap, et al.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention|March 1, 2014
Biomechanically driven registration of pre- to intra-operative 3D images for laparoscopic surgeryOzan Oktay, Li Zhang, Tommaso Mansi, et al.
Frontiers in Pediatrics|December 27, 2016
Regional Differences in End-Diastolic Volumes between 3D Echo and CMR in HLHS PatientsAlberto Gomez, Ozan Oktay, Daniel Rueckert, et al.
Scientific Reports|February 14, 2020
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK BiobankGiacomo Tarroni, Wenjia Bai, Ozan Oktay, et al.
Medical Image Analysis|February 21, 2019
Evaluating reinforcement learning agents for anatomical landmark detectionAmir Alansary, Ozan Oktay, Yuanwei Li, et al.
IEEE Transactions on Medical Imaging|January 6, 2017
Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac ImagesOzan Oktay, Wenjia Bai, Ricardo Guerrero, et al.
IEEE Transactions on Medical Imaging|November 16, 2016
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural NetworksMartin Rajchl, Matthew C H Lee, Ozan Oktay, et al.
Pageof 2