B
Bernhard Kainz
Researcher at Imperial College London
Publications - 113
Citations - 8561
Bernhard Kainz is an academic researcher from Imperial College London. The author has contributed to research in topics: Segmentation & Rendering (computer graphics). The author has an hindex of 30, co-authored 111 publications receiving 5585 citations. Previous affiliations of Bernhard Kainz include Graz University of Technology & King's College London.
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Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay,Jo Schlemper,Loic Le Folgoc,Matthew C. H. Lee,Mattias P. Heinrich,Kazunari Misawa,Kensaku Mori,Steven McDonagh,Nils Y. Hammerla,Bernhard Kainz,Ben Glocker,Daniel Rueckert +11 more
TL;DR: A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
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Attention gated networks: Learning to leverage salient regions in medical images.
Jo Schlemper,Ozan Oktay,Michiel Schaap,Mattias P. Heinrich,Bernhard Kainz,Ben Glocker,Daniel Rueckert +6 more
TL;DR: Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency.
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Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay,Enzo Ferrante,Konstantinos Kamnitsas,Mattias P. Heinrich,Wenjia Bai,Jose Caballero,Stuart A. Cook,Antonio de Marvao,Timothy J W Dawes,Declan P. O'Regan,Bernhard Kainz,Ben Glocker,Daniel Rueckert +12 more
TL;DR: In this article, a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularization model, which is trained end-to-end, encourages models to follow the global anatomical properties of the underlying anatomy via learnt non-linear representations of the shape.
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Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
Ozan Oktay,Enzo Ferrante,Konstantinos Kamnitsas,Mattias P. Heinrich,Wenjia Bai,Jose Caballero,Stuart A. Cook,Antonio de Marvao,Timothy J W Dawes,Declan P. O'Regan,Bernhard Kainz,Ben Glocker,Daniel Rueckert +12 more
TL;DR: This work proposes a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end and demonstrates how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
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DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Martin Rajchl,Matthew C. H. Lee,Ozan Oktay,Konstantinos Kamnitsas,Jonathan Passerat-Palmbach,Wenjia Bai,Mellisa Damodaram,Mary A. Rutherford,Joseph V. Hajnal,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: DeepCut as discussed by the authors proposes a method to obtain pixelwise object segmentations given an image dataset labeled weak annotations, in our case bounding boxes, by training a neural network classifier from bounding box annotations.