Our NeuroNet paper received the Philips Impact Award at the 1st International Conference on Medical Imaging with Deep Learning 2018. NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker Paper: https://arxiv.org/abs/1806.04224 Code & Models: https://github.com/DLTK/models/tree/master/ukbb_neuronet_brain_segmentation
Huge success for a team of researchers from the Biomedical Image Analysis group at the Department of Computing. The team of 11 PhD students, post-docs and academics ranked top on the prestigious, international computational challenge on brain tumour segmentation (BraTS). The BraTS challenge did run for the sixth time and this year more than 50 … Continued
We are pleased to announce the release of the DLTK. DLTK is a neural networks toolkit written in python, on top of Tensorflow. Its modular architecture is closely inspired by Deepmind sonnet and it was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its … Continued
We are very excited to be placed among five finalists for the 2016 NVIDIA Global Impact Award with our work on Deep Learning for Brain Lesion Segmentation. The credit goes to PhD student Konstantinos Kamnitsas. More details about the work can be found here.
Members of the BioMedIA group have been very successful at this year’s MICCAI computational challenges.PhD student Konstantinos Kamnitsas has won the ISLES 2015 challenge with his approach based on Multi-Scale Convolutional Neural Nets. Master student Isabel Lopez was ranked 2nd in the Automatic Intervertebral Disc Segmentation from MRI with her approach using classification forests with … Continued
We have been granted an Innovative Engineering for Health Award of £10 million funded by the Wellcome Trust and the Engineering and Physical Sciences Research Council (EPSRC) to develop new computer guided ultrasound technologies that will allow screening of fetal abnormalities in an automated and uniform fashion. We will use state of the art techniques … Continued