Search Results for author:
Found 13 papers, 8 papers with code
Patch-based Evaluation of Image Segmentation
The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D Medical Imaging Segmentation
Brain Lesion Segmentation From Mri
+3
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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.
Ranked #3 on
Image Super-Resolution
on VggFace2 - 8x upscaling
Is the deconvolution layer the same as a convolutional layer?
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Convolutional neural networks have enabled accurate image super-resolution in real-time.
Ranked #11 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
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.
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Compared to sub-pixel convolution initialized with schemes designed for standard convolution kernels, it is free from checkerboard artifacts immediately after initialization.
Employing Weak Annotations for Medical Image Analysis Problems
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.
Generative adversarial networks and adversarial methods in biomedical image analysis
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.
Generative Image Translation for Data Augmentation of Bone Lesion Pathology
Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
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.
unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features
We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features.
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes.
