Deep learning and Bayesian inference for medical imaging

Project Information

DeeBMED

Grant agreement ID: 702666

DOI

10.3030/702666

Project closed

EC signature date 17 February 2016

Start date 1 October 2016

End date 30 September 2018

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 177 598,80

EU contribution

€ 177 598,80

Coordinated by

UNIVERSITEIT VAN AMSTERDAM
Netherlands

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Conference proceedings (11)

VAE with a VampPrior

Author(s): Jakub Tomczak, Max Welling
Published in: Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, Issue PMLR 84, 2018, Page(s) 1214-1223
Publisher: Journal of Machine Learning Research

Improving variational auto-encoders using householder flow

Author(s): Jakub M Tomczak, Max Welling
Published in: Bayesian Deep Learning Workshop @ NIPS 2016, 2016, Page(s) 8
Publisher: Bayesian Deep Learning Workshop @ NIPS 2016

Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow

Author(s): Jakub M. Tomczak, M Welling
Published in: Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, 2017, Page(s) 162-164
Publisher: TU/e

A deep multiple instance model to predict prostate cancer metastasis from nuclear morphology

Author(s): Nathan Ing, Jakub M Tomczak, Eric Miller, Isla P Garraway, Max Welling, Beatrice S Knudsen, Arkadiusz Gertych
Published in: Medical Imaging with Deep Learning, 2018
Publisher: OpenReview

Deep Learning with Order-invariant Operator for Multi-instance Histopathology Classification

Author(s): Jakub Tomczak, Maximilian Ilse and Max Welling
Published in: MEDICAL IMAGING MEETS NIPS 2017, 2017
Publisher: arXiv

Sylvester Normalizing Flows for Variational Inference

Author(s): Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling
Published in: Uncertainty in Artificial Intelligence Proceedings of the Thirty-Fourth Conference (2018), 2018
Publisher: UAI 2018

Attention-based Deep Multiple Instance Learning

Author(s): Maximilian Ilse, Jakub Tomczak, Max Welling
Published in: Volume 80: International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden, Issue PMLR 80, 2018, Page(s) 2127-2136
Publisher: PMLR

Hierarchical VampPrior Variational Fair Auto-Encoder

Author(s): Philip Botros and Jakub Tomczak
Published in: Theoretical Foundations and Applications of Deep Generative Models @ ICML 2018, 2018
Publisher: Theoretical Foundations and Applications of Deep Generative Models @ ICML 2018

Variational Inference with Orthogonal Normalizing Flows

Author(s): Leonard Hasenclever, Jakub Tomczak, Rianne van den Berg and Max Welling
Published in: Bayesian Deep Learning @ NIPS 2017, 2017
Publisher: Bayesian Deep Learning @ NIPS 2017

Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach

Author(s): 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
Published in: Medical Imaging with Deep Learning, 2018
Publisher: OpenReview

Hyperspherical Variational Auto-Encoders

Author(s): Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
Published in: Uncertainty in Artificial Intelligence Proceedings of the Thirty-Fourth Conference (2018), 2018
Publisher: UAI 2018