Publications

Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation

Brennan Nichyporuk

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Sotirios A. Tsaftaris

Douglas Arnold

Tal Arbel

2021-09-21

Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (publié)

Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

Reza Azad

Lucas Rouhier

Julien Cohen-Adad

2021-09-21

Machine Learning in Medical Imaging (publié)

An Autonomous Probing System for Collecting Measurements at Depth from Small Surface Vehicles

Yuying Huang

Yiming Yao

Johanna Hansen

Jeremy Mallette

Sandeep Manjanna

Gregory Dudek

David Meger

2021-09-20

OCEANS 2021: San Diego – Porto (publié)

Connectivity alterations in autism reflect functional idiosyncrasy

Oualid Benkarim

Casey Paquola

Bo-yong Park

Seok-Jun Hong

Jessica Royer

Reinder Vos de Wael

Sara Lariviere

Sofie Valk

Laurent Mottron

Boris C Bernhardt

2021-09-15

Communications Biology (publié)

Social isolation is linked to classical risk factors of Alzheimer’s disease-related dementias

Kimia Shafighi

Sylvia Villeneuve

P. Rosa-Neto

AmanPreet Badhwar

Judes Poirier

Vaibhav Sharma

Yasser Iturria-Medina

Patricia P. Silveira

Laurette Dubé

David C. Glahn

Alzheimer’s disease and related dementias is a major public health burden – compounding over upcoming years due to longevity. Recently, … (voir plus)clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals’ social capital and various indicators of Alzheimer’s disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.

2021-09-15

bioRxiv (preprint)

Haptics-based Curiosity for Sparse-reward Tasks

Sai Rajeswar

Cyril Ibrahim

Nitin Surya

Florian Golemo

David Vazquez

Aaron Courville

Pedro O. Pinheiro

Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary for tasks tha… (voir plus)t involve contact-rich motion. In this work, we leverage surprise from mismatches in haptics feedback to guide exploration in hard sparse-reward reinforcement learning tasks. Our approach, Haptics-based Curiosity (\method{}), learns what visible objects interactions are supposed to ``feel" like. We encourage exploration by rewarding interactions where the expectation and the experience do not match. We test our approach on a range of haptics-intensive robot arm tasks (e.g. pushing objects, opening doors), which we also release as part of this work. Across multiple experiments in a simulated setting, we demonstrate that our method is able to learn these difficult tasks through sparse reward and curiosity alone. We compare our cross-modal approach to single-modality (haptics- or vision-only) approaches as well as other curiosity-based methods and find that our method performs better and is more sample-efficient.

2021-09-13

robot-learning.org/CoRL/2021/Conference (poster)

proceedings.mlr.press

openreview.net

Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge

Uzay Macar

Enamundram Naga Karthik

Charley Gros

Andreanne Lemay

Julien Cohen-Adad

This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion … (voir plus)Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.

2021-09-12

ArXiv (prépublication)

Decision Models and Technology Can Help Psychiatry Develop Biomarkers

Daniel S. Barron

Justin T. Baker

Kristin S. Budde

Simon B. Eickhoff

Karl J. Friston

Peter T. Fox

Paul Geha

Stephen Heisig

Avram J. Holmes

Jukka-Pekka Onnela

Albert Powers

David Silbersweig

John H. Krystal

2021-09-09

Frontiers in Psychiatry (publié)

On the estimation of discrete choice models to capture irrational customer behaviors

Sanjay Dominik Jena

Andrea Lodi

Claudio Sole

The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (voir plus)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.

2021-09-08

ArXiv (preprint)

Simple Video Generation using Neural ODEs

David Kanaa

Vikram Voleti

Samira Ebrahimi Kahou

Chris Pal

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely chall… (voir plus)enging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.

2021-09-07

ArXiv (prépublication)

Social belonging: Brain structure and function is linked to membership in sports teams, religious groups and social clubs

Carolin Kieckhaefer

Leonhard Schilbach

2021-09-07

bioRxiv (prépublication)