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DeeBMED

Deep learning and Bayesian inference for medical imaging

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 DeeBMED project word cloud

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

nowadays    data    pet    examples    networks    amsterdam    scans    enormous    prof    consuming    combines    max    with    deebmed    source    time    gaussian    robustness    amount    automatic    cardiovascular    images    learning    disease    representing    supervision    framework    diagnostics    nets    weights    powerful    relationships    quantity    diabetes    imaging    prone    predict    disorders    techniques    union    welling    convolutional    millions    first    civilization    identification    modern    image    accessible    variables    automation    therapy    bayesian    probabilistic    dimension    decrease    reduce    transformations    radiologists    cancer    neural    feedforward    expensive    multimodality    medical    remedy    deep    small    incidences    latent    distillation    deaths    size    tool    university    label    dnn    labeled    model    strategy    western    scan    cope    dropout    drastically    overfitting    institutes    hidden    ct    mri    appear    diseases    stage    progression   

Project "DeeBMED" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITEIT VAN AMSTERDAM 

Organization address
address: SPUI 21
city: AMSTERDAM
postcode: 1012WX
website: www.uva.nl

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 Netherlands [NL]
 Project website https://jmtomczak.github.io/deebmed.html
 Total cost 177˙598 €
 EC max contribution 177˙598 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-10-01   to  2018-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITEIT VAN AMSTERDAM NL (AMSTERDAM) coordinator 177˙598.00

Map

 Project objective

Diseases characteristic for modern western civilization, such as cancer, diabetes or cardiovascular disorders, lead to millions of deaths per year in the European Union. In order to decrease this enormous quantity, medical imaging should be widely available at early diagnostics and every stage of a therapy. Nowadays, there are various diagnostics techniques including CT, PET, MRI, however, analysis of a medical image is time-consuming and expensive. Development of new effective automatic tool for medical imaging will appear a new strategy in highly specific control of incidences and disease progression. The aim of the DeeBMED project is to develop powerful automatic medical imaging tool that can cope with main problems associated with complex images like medical scans: multimodality of data distribution, large number of dimension and small number of examples, small amount of labeled data, multi-source learning, and robustness to transformations. In this project I will propose a probabilistic framework that combines different deep neural networks (DNN), such as feedforward nets, convolutional nets, Gaussian processes. I will apply DNN to model probabilistic relationships among a medical scan, a disease label, and hidden variables representing latent factors in data. In the case of a small sample size DNN are prone to overfitting. A possible remedy for that is Bayesian learning, however, it is still challenging how to apply it to DNN. In this project I will use various approaches: modelling weights uncertainty, Dropout, Bayesian Distillation. As the result I predict identification of the first highly effective medical imaging analysis tool that in the future will be widely used by radiologists in medical institutes in the whole EU. Novel automation will drastically reduce time and costs of analysis and provide more accessible diagnostics. The project will be carried out at the University of Amsterdam, under the supervision of Prof. Max Welling.

 Publications

year authors and title journal last update
List of publications.
2018 Maximilian Ilse, Jakub Tomczak, Max Welling
Attention-based Deep Multiple Instance Learning
published pages: 2127-2136, ISSN: , DOI:
Volume 80: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden PMLR 80 2019-05-14
2017 Jakub Tomczak, Maximilian Ilse and Max Welling
Deep Learning with Order-invariant Operator for Multi-instance Histopathology Classification
published pages: , ISSN: , DOI:
MEDICAL IMAGING MEETS NIPS 2017 2019-05-14
2017 Jakub M. Tomczak, M Welling
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
published pages: 162-164, ISSN: , DOI:
Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning 2019-05-14
2018 Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
Hyperspherical Variational Auto-Encoders
published pages: , ISSN: , DOI:
Uncertainty in Artificial Intelligence Proceedings of the Thirty-Fourth Conference (2018) 2019-05-14
2018 Jakub M Tomczak, Maximilian Ilse, Max Welling, Marnix Jansen, Helen G Coleman, Marit Lucas, Kikki de Laat, Martijn de Bruin, Henk Marquering, Myrtle J van der Wel, Onno J de Boer, C Dilara Savci Heijink, Sybren L Meijer
Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach
published pages: , ISSN: , DOI:
Medical Imaging with Deep Learning 2019-05-14
2018 Jakub Tomczak, Max Welling
VAE with a VampPrior
published pages: 1214-1223, ISSN: , DOI:
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics PMLR 84 2019-05-14
2016 Jakub M Tomczak, Max Welling
Improving variational auto-encoders using householder flow
published pages: 8, ISSN: , DOI:
Bayesian Deep Learning Workshop @ NIPS 2016 2019-05-14
2017 Leonard Hasenclever, Jakub Tomczak, Rianne van den Berg and Max Welling
Variational Inference with Orthogonal Normalizing Flows
published pages: , ISSN: , DOI:
Bayesian Deep Learning @ NIPS 2017 2019-05-14
2018 Philip Botros and Jakub Tomczak
Hierarchical VampPrior Variational Fair Auto-Encoder
published pages: , ISSN: , DOI:
Theoretical Foundations and Applications of Deep Generative Models @ ICML 2018 2019-05-14
2018 Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling
Sylvester Normalizing Flows for Variational Inference
published pages: , ISSN: , DOI:
Uncertainty in Artificial Intelligence Proceedings of the Thirty-Fourth Conference (2018) 2019-05-14
2018 Nathan Ing, Jakub M Tomczak, Eric Miller, Isla P Garraway, Max Welling, Beatrice S Knudsen, Arkadiusz Gertych
A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology
published pages: , ISSN: , DOI:
Medical Imaging with Deep Learning 2019-05-14

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The information about "DEEBMED" are provided by the European Opendata Portal: CORDIS opendata.

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