Search Results for author: Christian Ledig

Found 11 papers, 5 papers with code

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

1 code implementation28 Jun 2019 Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert

At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.

Generative Image Translation for Data Augmentation of Bone Lesion Pathology

no code implementations6 Feb 2019 Anant Gupta, Srivas Venkatesh, Sumit Chopra, Christian Ledig

Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases.

Data Augmentation General Classification +3

Generative adversarial networks and adversarial methods in biomedical image analysis

no code implementations24 Oct 2018 Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum

Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.

Employing Weak Annotations for Medical Image Analysis Problems

no code implementations21 Aug 2017 Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku MORI, Daniel Rueckert

To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.

Computed Tomography (CT) Liver Segmentation

Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize

3 code implementations10 Jul 2017 Andrew Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi

Compared to sub-pixel convolution initialized with schemes designed for standard convolution kernels, it is free from checkerboard artifacts immediately after initialization.

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

no code implementations28 Dec 2016 Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.

Lesion Segmentation Unsupervised Domain Adaptation

Is the deconvolution layer the same as a convolutional layer?

4 code implementations22 Sep 2016 Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, Zehan Wang

In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

126 code implementations CVPR 2017 Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.

Image Super-Resolution

Patch-based Evaluation of Image Segmentation

no code implementations CVPR 2014 Christian Ledig, Wenzhe Shi, Wenjia Bai, Daniel Rueckert

The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias.

Hippocampus Semantic Segmentation

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