Publications

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
Amar Kumar
Anjun Hu
Brennan Nichyporuk
Jean-Pierre R. Falet
Douglas Arnold
Sotirios A. Tsaftaris
Tal Arbel
2022-08-02
ArXiv (prépublication)
doi.org
arxiv.org
Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment
Yesukhei Jagvaral
François Lanusse
Sukhdeep Singh
Rachel Mandelbaum
Siamak Ravanbakhsh
Duncan Campbell
In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations ar… (voir plus)e required. In particular, the tendency of galaxies to naturally align towards overdensities, an effect called intrinsic alignments (IA), can be a major source of systematics in the weak lensing analysis. As the details of galaxy formation and evolution relevant to IA cannot be simulated in practice on such volumes, we propose as an alternative a Deep Generative Model. This model is trained on the IllustrisTNG-100 simulation and is capable of sampling the orientations of a population of galaxies so as to recover the correct alignments. In our approach, we model the cosmic web as a set of graphs, where the graphs are constructed for each halo, and galaxy orientations as a signal on those graphs. The generative model is implemented on a Generative Adversarial Network architecture and uses specifically designed Graph-Convolutional Networks sensitive to the relative 3D positions of the vertices. Given (sub)halo masses and tidal fields, the model is able to learn and predict scalar features such as galaxy and dark matter subhalo shapes; and more importantly, vector features such as the 3D orientation of the major axis of the ellipsoid and the complex 2D ellipticities. For correlations of 3D orientations the model is in good quantitative agreement with the measured values from the simulation, except for at very small and transition scales. For correlations of 2D ellipticities, the model is in good quantitative agreement with the measured values from the simulation on all scales. Additionally, the model is able to capture the dependence of IA on mass, morphological type and central/satellite type.
2022-08-01
Monthly Notices of the Royal Astronomical Society (inconnu)
doi.org
arxiv.org
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Samira Abbasgholizadeh Rahimi
Michelle Cwintal
Yuhui Huang
Pooria Ghadiri
Roland Grad
Dan Poenaru
Genevieve Gore
Herve Tchala Vignon Zomahoun
France Légaré
Pierre Pluye
Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared decision m… (voir plus)aking (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
2022-07-31
JMIR Medical Informatics (publié)
doi.org
Definitive Care for Severely Injured Children in Quebec
Mélyssa Fortin
Zoe Atsaidis
Brent Hopkins
Etienne St-Louis
Elena Guadagno
Debbie Friedman
Dan Poenaru
2022-07-31
Injury (publié)
doi.org
Existing eHealth Solutions for Older Adults Living With Neurocognitive Disorders (Mild and Major) or Dementia and Their Informal Caregivers: Protocol for an Environmental Scan
Ambily Jose
Maxime Sasseville
Samantha Dequanter
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Samira Abbasgholizadeh Rahimi
Ronald Buyl
Marie-Pierre Gagnon
Dementia is one of the main public health priorities for current and future societies worldwide. Over the past years, eHealth solutions have… (voir plus) added numerous promising solutions to enhance the health and wellness of people living with dementia-related cognitive problems and their primary caregivers. Previous studies have shown that an environmental scan identifies the knowledge-to-action gap meaningfully. This paper presents the protocol of an environmental scan to monitor the currently available eHealth solutions targeting dementia and other neurocognitive disorders against selected attributes. This study aims to identify the characteristics of currently available eHealth solutions recommended for older adults with cognitive problems and their informal caregivers. To inform the recommendations regarding eHealth solutions for these people, it is important to obtain a comprehensive view of currently available technologies and document their outcomes and conditions of success. We will perform an environmental scan of available eHealth solutions for older adults with cognitive impairment or dementia and their informal caregivers. Potential solutions will be initially identified from a previous systematic review. We will also conduct targeted searches for gray literature on Google and specialized websites covering the regions of Canada and Europe. Technological tools will be scanned based on a preformatted extraction grid. The relevance and efficiency based on the selected attributes will be assessed. We will prioritize relevant solutions based on the needs and preferences identified from a qualitative study among older adults with cognitive impairment or dementia and their informal caregivers. This environmental scan will identify eHealth solutions that are currently available and scientifically appraised for older adults with cognitive impairment or dementia and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate eHealth solutions according to their needs and preferences based on trustable information. DERR1-10.2196/41015
2022-07-31
JMIR Research Protocols (publié)
doi.org
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh
C. M. Urry
Amrit Rau
Laurence Perreault-Levasseur
Miles Cranmer
Kevin Schawinski
Dominic Stark
Chuan Tian
Ryan Ofman
Tonima Tasnim Ananna
Connor Auge
N. Cappelluti
D. B. Sanders
Ezequiel Treister
We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large num… (voir plus)bers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L B /L T ), effective radius (R e ), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match z 0.25 galaxies in Hyper Suprime-Cam Wide g-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 in L B /L T , 0.″17 (∼7%) in R e , and 6.3 × 104 nJy (∼1%) in F. GaMPEN's predicted uncertainties are well calibrated and accurate (5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We a
2022-07-31
The Astrophysical Journal (publié)
doi.org
arxiv.org
Good scientific practice in EEG and MEG research: Progress and perspectives
Guiomar Niso
Laurens R. Krol
Etienne Combrisson
A. Sophie Dubarry
Madison A. Elliott
Clément François
Yseult Héjja-Brichard
Sophie K. Herbst
Karim Jerbi
Vanja Kovic
Katia Lehongre
Steven J. Luck
Manuel Mercier
John C. Mosher
Yuri G. Pavlov
Aina Puce
Antonio Schettino
Daniele Schön
Walter Sinnott-Armstrong
Bertille Somon … (voir 6 de plus)
Anđela Šoškić
Suzy J. Styles
Roni Tibon
Martina G. Vilas
Marijn van Vliet
Maximilien Chaumon
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce w… (voir plus)ork that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
2022-07-31
NeuroImage (publié)
doi.org
Induced pluripotent stem cells display a distinct set of MHC I-associated peptides shared by human cancers
Anca Apavaloaei
Leslie Hesnard
Marie-Pierre Hardy
Basma Benabdallah
Gregory Ehx
Catherine Thériault
Jean-Philippe Laverdure
Chantal Durette
Joël Lanoix
Mathieu Courcelles
Nandita Noronha
Kapil Dev Chauhan
Sébastien Lemieux
Christian Beauséjour
Mick Bhatia
Pierre Thibault
Claude Perreault
2022-07-31
Cell Reports (publié)
doi.org
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta
Changjian Shui
Brennan Nichyporuk
Tal Arbel
2022-07-31
ArXiv (prépublication)
doi.org
arxiv.org
Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David L Buckeridge
Marc Brisson
Beate Sander
Adrienne Chan
Aman Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu-Giroux
Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in intensive cares … (voir plus)units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. We used population-based provincial hospitalization data from Ontario and Québec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Using all hospitalizations from Ontario (N=26,541) and Québec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Québec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Québec by 3-6 days. In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
2022-07-31
International Journal of Infectious Diseases (publié)
doi.org
A portrait of the different configurations between digitally-enabled innovations and climate governance
Pierre J. C. Chuard
Jennifer Garard
Karsten A. Schulz
Nilushi Kumarasinghe
David Rolnick
Damon Matthews
2022-07-31
Earth System Governance (publié)
doi.org
Single‐pass stratified importance resampling
Ege Ciklabakkal
Adrien Gruson
Iliyan Georgiev
D. Nowrouzezahrai
Toshiya Hachisuka
Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired t… (voir plus)arget. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
2022-07-29
Computer Graphics Forum (publié)
doi.org