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MIRA SIGNED

Next Generation Machine Intelligence for Medical Image Representation and Analysis

Total Cost €

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EC-Contrib. €

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Partnership

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 MIRA project word cloud

Explore the words cloud of the MIRA project. It provides you a very rough idea of what is the project "MIRA" about.

human    analysing    performance    harvest    shared    interpretable    insights    models    population    abnormality    intelligent    devoted    patterns    pathological    interpretation    interpreting    tools    experts    environmental    critical    learned    ingredients    disease    trustworthy    extraction    algorithms    undetected    detection    databases    medical    risk    solving    overarching    automatically    contrast    transform    combined    organs    pathology    jointly    diseases    push    machines    healthcare    linking    image    machine    tackle    construct    computational    demographics    anatomical    complexity    learning    representation    generation    powerful    primarily    probing    century    reducing    anatomy    imaging    world    limit    phenotypes    genetics    itself    capture    determinants    lifestyle    signs    scans    subtle    super    clinical    redefine    expertise    representations    intelligence    did    missing    data    statistical    last    volume    attempts    clinically    genetic    multiple    techniques    images    leverage   

Project "MIRA" data sheet

The following table provides information about the project.

Coordinator
IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE 

Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
city: LONDON
postcode: SW7 2AZ
website: http://www.imperial.ac.uk/

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://project-mira.eu
 Total cost 1˙499˙292 €
 EC max contribution 1˙499˙292 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-02-01   to  2023-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE UK (LONDON) coordinator 1˙499˙292.00

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 Project objective

Machines capable of analysing and interpreting medical scans with super-human performance would transform healthcare as much as medical imaging itself did over the last century. With an increasing complexity and volume of data the interpretation of images and extraction of clinically useful information push human abilities to the limit. There is high risk that critical patterns of disease go undetected. We require powerful and trustworthy computational tools based on machine intelligence to support experts and go beyond human performance to tackle the major challenges in clinical practice. Two key ingredients are currently missing: 1) interpretable statistical representations that capture important information while reducing complexity; 2) intelligent algorithms that leverage knowledge across multiple tasks to solve the most challenging problems such as early detection of pathology.

This project is devoted to redefine the state-of-the-art in medical image analysis by developing a new generation of machine intelligence using powerful techniques of representation learning. Key to the project is its unique access to some of the largest and most comprehensive imaging databases combined with world-leading expertise in machine learning and medical imaging. An overarching objective is to harvest information from population data to construct what will be the most advanced statistical models of anatomy. In contrast to previous attempts that focus primarily on specific organs or pathology, here shared representations are learned from highly complex data by jointly solving multiple tasks. Linking the representations with demographics, lifestyle, genetics and disease allows probing of genetic and environmental determinants related to specific anatomical and pathological phenotypes across organs. This will provide insights into complex diseases, and enables a novel approach to abnormality detection that aims to automatically find subtle signs of pathology in new medical scans.

 Publications

year authors and title journal last update
List of publications.
2019 Loïc Le Folgoc, Daniel C. Castro, Jeremy Tan, Bishesh Khanal, Konstantinos Kamnitsas, Ian Walker, Amir Alansary, Ben Glocker
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
published pages: 221-234, ISSN: 9783-0302, DOI: 10.1007/978-3-030-20351-1_17
Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 11492 2019-10-29
2018 Chaitanya Baweja, Ben Glocker, Konstantinos Kamnitsas
Towards continual learning in medical imaging
published pages: , ISSN: , DOI:
Workshop Medical Imaging meets NeurIPS 2019-10-08
2019 Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, José Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. Matthews, Daniel Rueckert, Ben Glocker
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
published pages: , ISSN: 1532-429X, DOI: 10.1186/s12968-019-0523-x
Journal of Cardiovascular Magnetic Resonance 21/1 2019-10-08
2018 Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
published pages: , ISSN: , DOI:
International Workshop on Machine Learning in Medical imaging (MLMI) 2019-06-11
2018 Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
published pages: , ISSN: , DOI:
International Conference on Medical Imaging with Deep Learning (MIDL) 2019-06-12
2018 Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori
Semi-Supervised Learning via Compact Latent Space Clustering
published pages: 2464-2473, ISSN: , DOI:
Proceedings of the 35th International Conference on Machine Learning 80 2019-06-11
2018 Daniel C. Castro, Ben Glocker
Nonparametric Density Flows for MRI Intensity Normalisation
published pages: , ISSN: , DOI:
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019-06-11
2018 Robert Robinson, Ozan Oktay, Wenjia Bai, Vanya Valindria, Mihir Sanghvi, Nay Aung, José Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron Lee, Valentina Carapella, Young Jin Kim, Bernhard Kainz, Stefan Piechnik, Stefan Neubauer, Steffen Petersen, Chris Page, Daniel Rueckert, Ben Glocker
Real-time Prediction of Segmentation Quality
published pages: , ISSN: , DOI:
nternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019-06-11
2018 Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
published pages: , ISSN: , DOI:
International Conference on Medical Imaging with Deep Learning (MIDL) 2019-06-11
2019 Ian Walker, Ben Glocker
Graph Convolutional Gaussian Processes
published pages: , ISSN: , DOI:
Proceedings of the 36th International Conference on Machine Learning 2019-06-06

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