Publications

Interpretable domain adaptation using unsupervised feature selection on pre-trained source models
Luxin Zhang
Pascal Germain
Yacine Kessaci
C. Biernacki
2022-08-31
Neurocomputing (published)
doi.org
A novel domain adaptation theory with Jensen-Shannon divergence
Changjian Shui
Qi CHEN
Jun Wen
Fan Zhou
Christian Gagné
Boyu Wang
2022-08-31
Knowledge-Based Systems (published)
doi.org
The load planning and sequencing problem for double-stack trains
Moritz Ruf
Jean-François Cordeau
Emma Frejinger
2022-08-31
Journal of Rail Transport Planning & Management (published)
doi.org
Two types of human TCR differentially regulate reactivity to self and non-self antigens
Assya Trofimov
Philippe Brouillard
Jean-David Larouche
Jonathan Séguin
Jean-Philippe Laverdure
Ann Brasey
Gregory Ehx
Denis-Claude Roy
Lambert Busque
Silvy Lachance
Sébastien Lemieux
Claude Perreault
Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and funct… (see more)ionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes.
2022-08-31
iScience (published)
doi.org
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta
Angelos Filos
Ujjwal Baid
Chiharu Sako
Richard McKinley
Michael Rebsamen
Katrin Datwyler
Raphaël Meier
Piotr Radojewski
Gowtham Krishnan Murugesan
Sahil Nalawade
Chandan Ganesh
Ben Wagner
Fang Yu
Baowei Fei
Ananth J. Madhuranthakam
Joseph A. Maldjian
Laura Daza
Catalina Gómez
Pablo Arbeláez … (see 72 more)
Chengliang Dai
Shuo Wang
Hadrien Reynaud
Yuan-han Mo
Elsa D. Angelini
Yike Guo
Wenjia Bai
Subhashis Banerjee
Lin-min Pei
Murat AK
Sarahi Rosas-González
Ilyess Zemmoura
Clovis Tauber
Minh H. Vu
Tufve Nyholm
Tommy Löfstedt
Laura Mora Ballestar
Verónica Vilaplana
Hugh McHugh
Gonzalo D. Maso Talou
Alan Wang
Jay Patel
Ken Chang
Katharina Hoebel
Mishka Gidwani
Nishanth Arun
Sharut Gupta
Mehak Aggarwal
Praveer Singh
Elizabeth R. Gerstner
Jayashree Kalpathy-Cramer
Nicolas Boutry
Alexis Huard
Lasitha Vidyaratne
Md Monibor Rahman
Khan M. Iftekharuddin
Joseph Chazalon
Élodie Puybareau
Guillaume Tochon
Jun Ma
Mariano Cabezas
Xavier Lladó
Arnau Oliver
Liliana Patricia Marlés Valencia
Sergi Valverde
Mehdi Amian
Mohammadreza Soltaninejad
Andriy Myronenko
Ali Hatamizadeh
Xue Feng
Fang Yu
Nicholas Tustison
Craig H. Meyer
Nisarg A. Shah
Sanjay N. Talbar
Marc‐André Weber
Abhishek Mahajan
Andras Jakab
Roland Wiest
Hassan M. Fathallah‐Shaykh
Arash Nazeri
Mikhail Milchenko
Daniel C. Marcus
Aikaterini Kotrotsou
Rivka R. Colen
John Freymann
Justin Kirby
Christos Davatzikos
Bjoern Menze
Spyridon Bakas
Yarin Gal
Tal Arbel
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain… (see more) Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.
2022-08-25
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org
Fractal impedance for passive controllers: a framework for interaction robotics
Keyhan Kouhkiloui Babarahmati
Carlo Tiseo
Joshua Smith
Hsiu-Chin Lin
M. S. Erden
Michael Nalin Mistry
2022-08-24
Nonlinear Dynamics (published)
doi.org
arxiv.org
Learning Shared Neural Manifolds from Multi-Subject fMRI Data
Jessie Huang
Jessie Huang
Erica L. Busch
Tom Wallenstein
Michal Gerasimiuk
Andrew Benz
Guillaume Lajoie
Guy Wolf
Nicholas B. Turk-Browne
Smita Krishnaswamy
Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between in… (see more)dividuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future.
2022-08-21
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP) (published)
doi.org
openreview.net
Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells
Zhaoguan Wu
Éric Bonneil
Michael Belford
Cornelia Boeser
Maria Virginia Ruiz Cuevas
Sébastien Lemieux
Jean-Jacques Dunyach
Pierre Thibault
The sensitivity and depth of proteomic analyses are limited by isobaric ions and interferences that preclude the identification of low abund… (see more)ance peptides. Extensive sample fractionation is often required to extend proteome coverage when sample amount is not a limitation. Ion mobility devices provide a viable alternate approach to resolve confounding ions and improve peak capacity and mass spectrometry (MS) sensitivity. Here, we report the integration of differential ion mobility with segmented ion fractionation (SIFT) to enhance the comprehensiveness of proteomic analyses. The combination of differential ion mobility and SIFT, where narrow windows of ∼m/z 100 are acquired in turn, is found particularly advantageous in the analysis of protein digests and typically provided more than 60% gain in identification compared to conventional single-shot LC–MS/MS. The application of this approach is further demonstrated for the analysis of tryptic digests from different colorectal cancer cell lines where the enhanced sensitivity enabled the identification of single amino acid variants that were correlated with the corresponding transcriptomic data sets.
2022-08-21
Analytical Chemistry (published)
doi.org
Cascaded Video Generation for Videos In-the-Wild
Lluis Castrejon
Nicolas Ballas
Aaron Courville
Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascad… (see more)ed model for video generation which follows a coarse to fine approach. First our model generates a low resolution video, establishing the global scene structure, which is then refined by subsequent cascade levels operating at larger resolutions. We train each cascade level sequentially on partial views of the videos, which reduces the computational complexity of our model and makes it scalable to high-resolution videos with many frames. We empirically validate our approach on UCF101 and Kinetics-600, for which our model is competitive with the state-of-the-art. We further demonstrate the scaling capabilities of our model and train a three-level model on the BDD100K dataset which generates 256x256 pixels videos with 48 frames.
2022-08-20
2022 26th International Conference on Pattern Recognition (ICPR) (published)
doi.org
arxiv.org
Stratifying the autistic phenotype using electrophysiological indices of social perception
Luke Mason
Carolin Moessnang
Christopher H. Chatham
Lindsay Ham
Julian Tillmann
Guillaume Dumas
Claire Ellis
Claire Leblond
Freddy Cliquet
Thomas Bourgeron
Christian Beckmann
Tony Charman
Beth Oakley
Tobias Banaschewski
Andreas Meyer-Lindenberg
Simon Baron-Cohen
Sven Bölte
Jan K. Buitelaar
Sarah Durston
Eva Loth … (see 7 more)
Bob Oranje
Antonio Persico
Flavio Dell’Acqua
Christine Ecker
Mark Johnson
Declan Murphy
Emily J. H. Jones
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heter… (see more)ogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD. Description N170 latency to faces relates to fusiform activity and ASD genetics, predicts social prognosis, and could improve power in clinical trials. Exploiting face processing in patients with ASD The heterogeneity observed in patients with autism spectrum disorder (ASD) highlights the need for better patient stratification methods. Here, Mason et al. evaluated the use of the speed of early-stage face processing (N170 latency) for patient stratification and prognosis in subjects with ASD and age-matched healthy individuals. N170 latency was slower in individuals with ASD and correlated with response to faces measured with fMRI and with polygenic risk score. Among subjects with ASD, the N170 values stratified patients according to socialization prognosis and improved power in a simulated clinical trial. The results suggest that including N170 evaluation in patient stratification might help the design and development of patient-specific therapies for ASD.
2022-08-16
Science Translational Medicine (published)
doi.org
Pandemic policy assessment by artificial intelligence
Sirui Song
Xue Liu
Ying Li
Yang Yu
2022-08-15
Scientific Reports (published)
doi.org
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Florian Bordes
Randall Balestriero
P Vincent
Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used… (see more) to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
2022-08-14
TMLR (accepted)
openreview.net
openreview.net