Publications

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
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. 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. 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
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
Wiro Niessen
B. Emmer
2017-01-01
CMMI/RAMBO/SWITCH@MICCAI (published)
doi.org
Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau
Borja Balle
Joelle Pineau
We consider the problem of estimating multiple related functions computed by weighted automata~(WFA). We first present a natural notion of r… (see more)elatedness between WFAs by considering to which extent several WFAs can share a common underlying representation. We then introduce the model of vector-valued WFA which conveniently helps us formalize this notion of relatedness. Finally, we propose a spectral learning algorithm for vector-valued WFAs to tackle the multitask learning problem. By jointly learning multiple tasks in the form of a vector-valued WFA, our algorithm enforces the discovery of a representation space shared between tasks. The benefits of the proposed multitask approach are theoretically motivated and showcased through experiments on both synthetic and real world datasets.
PROCLIVITY PATTERNS IN ATTRIBUTED GRAPHS
Reihaneh Rabbany
Dhivya Eswaran
Christos Faloutsos
Artur Dubrawski
Many real world applications include information on both attributes of individual entities as well as relations between them, while there ex… (see more)ists an interplay between these attributes and relations. For example, in a typical social network, the similarity of individuals’ characteristics motivates them to form relations, a.k.a. social selection; whereas the characteristics of individuals may be affected by the characteristics of their relations, a.k.a. social influence. We can measure proclivity in networks by quantifying the correlation of nodal attributes and the structure [1]. Here, we are interested in a more fundamental study, to extend the basic statistics defined for graphs and draw parallels for the attributed graphs. More formally, an attributed graph is denoted by (A,X); where An×n is the adjacency matrix and encodes the relationships between the n nodes, and Xn×k is the attributes matrix –each row shows the feature vector of the corresponding node. Degree of a node encodes the number of its neighbors, computed as ki = ∑ j Aij . We can extend this notion to networks with binary attributes to the number of neighbors which share a particular attribute x, i.e. ki(x) = ∑ j Aijδ(Xj , x); where δ(Xj , x) = 1 iff node j has attribute x. Similar to the simple graphs, where the degree distribution is studied and showed to be heavy tail, here we can look at: 1) the degree distributions per attribute, 2) the joint probability distribution of any pair of attributes. Moreover, if we assume A(x1, x2) is the induced subgraph (or masked matrix of edges) with endpoints of values (x1, x2), i.e., A(x1, x2) = Aijδ(Xi, x1)δ(Xj , x2), then we can study and compare these distributions for the induced subgraph per each pair of attribute values. For example, Figure 1 shows the same general trend in the distribution of the original graph and the three possible induced subgraph.
2017-01-01
(published)
www.semanticscholar.org
Diet Networks: Thin Parameters for Fat Genomic
Adriana Romero Soriano
pierre luc carrier
Akram Erraqabi
Tristan Sylvain
Alex Auvolat
Etienne Dejoie
Marc-andr'e Legault
M. 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 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: 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), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation 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). 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.
2016-11-04
ArXiv (preprint)
arxiv.org
arxiv.org
A Multisensor Multi-Bernoulli Filter
Augustin-Alexandru Saucan
Mark Coates
Michael Rabbat
In this paper, we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget tracking. Measurements from multiple sensors are e… (see more)mployed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite set. An exact implementation of the MS-MeMBer update procedure is computationally intractable. We propose an efficient approximate implementation by using a greedy measurement partitioning mechanism. The proposed filter allows for Gaussian mixture or particle filter implementations. Numerical simulations conducted for both linear-Gaussian and nonlinear models highlight the improved accuracy of the MS-MeMBer filter and its reduced computational load with respect to the multisensor cardinalized probability hypothesis density filter and the iterated-corrector cardinality-balanced multi-Bernoulli filter especially for low probabilities of detection.
2016-09-16
ArXiv (preprint)
doi.org
arxiv.org
HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei
Nicolas Guizard
Nicolas Chapados
Yoshua Bengio
2016-07-18
ArXiv (preprint)
doi.org
arxiv.org