Publications

Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures
M. Cardoso
Tal Arbel
Xiongbiao Luo
Stefan Wesarg
Tobias Reichl
M. Ballester
Jonathan Mcleod
Klaus Dr. Drechsler
T. Peters
Marius Erdt
Kensaku Mori
M. Linguraru
Andreas Uhl
Cristina Oyarzun Laura
R. Shekhar
2017-01-01
Lecture Notes in Computer Science (published)
doi.org
Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures
M. Jorge Cardoso
Tal Arbel
Xiongbiao Luo
Stefan Wesarg
Tobias Reichl
M. Ballester
Jonathan Mcleod
Klaus Dr. Drechsler
T. Peters
Marius Erdt
Kensaku Mori
M. Linguraru
Andreas Uhl
Cristina Oyarzun Laura
R. Shekhar
2017-01-01
Lecture Notes in Computer Science (published)
doi.org
Computer-Assisted Conceptual Analysis of Textual Data as Applied to Philosophical Corpuses
Jean Guy Meunier
L. Chartrand
Jackie Cheung
Mathieu Valette
Marie-noëlle Bayle
2017-01-01
DH (published)
dblp.uni-trier.de
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
M. Jorge Cardoso
Tal Arbel
G. Carneiro
T. Syeda-Mahmood
J. Tavares
Mehdi Moradi
Andrew P. Bradley
Hayit Greenspan
J. Papa
Anant. Madabhushi
Jacinto C Nascimento
Jaime S. Cardoso
Vasileios Belagiannis
Zhi Lu
Faculdade Engenharia
2017-01-01
Lecture Notes in Computer Science (published)
doi.org
arxiv.org
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-André Legault
Marie-Pierre Dubé
Julie Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (see more) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net
Diet Networks: Thin Parameters for Fat Genomics
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-André Legault
Marie-Pierre Dubé
Julie Hussin
Yoshua Bengio
Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude… (see more) larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in medical research, more specifically in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer (number of input features times number of hidden units): each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed in data), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation (based on the feature's identity not its value) to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). This approach views the problem of producing the parameters associated with each feature as a multi-task learning problem. We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.
2017-01-01
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net
Fetal, Infant and Ophthalmic Medical Image Analysis
M. Jorge Cardoso
Tal Arbel
Andrew Melbourne
Hrvoje Bogunovic
Pim Moeskops
Xinjian Chen
Ernst Schwartz
M. Garvin
E. Robinson
E. Trucco
Michael Ebner
Yanwu Xu
Antonios Makropoulos
Adrien Desjardin
Tom Kamiel Magda Vercauteren
2017-01-01
Lecture Notes in Computer Science (published)
doi.org
Fetal, Infant and Ophthalmic Medical Image Analysis
M. Cardoso
Tal Arbel
Andrew Melbourne
Hrvoje Bogunovic
Pim Moeskops
Xinjian Chen
Ernst Schwartz
M. Garvin
E. Robinson
E. Trucco
Michael Ebner
Yanwu Xu
Antonios Makropoulos
Adrien Desjardin
Tom Kamiel Magda Vercauteren
2017-01-01
Lecture Notes in Computer Science (published)
doi.org
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics
M. Jorge Cardoso
Tal Arbel
Enzo Ferrante
Xavier Pennec
Adrian Dalca
Sarah Parisot
S. Joshi
Nematollah Batmanghelich
Aristeidis Sotiras
Mads Lenstrup Nielsen
Mert R. Sabuncu
Tom Fletcher
Li Shen
Stanley Durrleman
Stefan H. Sommer
2017-01-01
GRAIL/MFCA/MICGen@MICCAI (published)
doi.org
Hierarchical Methods of Moments
Matteo Ruffini
Guillaume Rabusseau
Borja Balle
Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal,… (see more) the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.
arxiv.org
Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
M. Jorge Cardoso
Tal Arbel
Su-Lin Lee
Veronika Cheplygina
Simone Balocco
Diana Mateus
Guillaume Zahnd
Lena Maier-Hein
Stefanie Demirci
Éric Granger
Luc Duong
M. Carbonneau
Shadi N. Albarqouni
G. Carneiro
2017-01-01
CVII-STENT/LABELS@MICCAI (published)
doi.org
Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment
M. Cardoso
Tal Arbel
Fei Gao
Bernhard Kainz
T. Walsum
Kuangyu Shi
Kanwal K. Bhatia
R. Peter
Tom Kamiel Magda Vercauteren
Mauricio Reyes
Adrian Dalca
Roland Wiest
W. Niessen
B. Emmer
2017-01-01
Lecture Notes in Computer Science (published)
doi.org