Publications

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
Brennan Nichyporuk
Justin Szeto
Douglas Arnold
Tal Arbel
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (voir plus)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.
2021-05-10
MIDL.io/2021/Conference/Short (poster)
openreview.net
openreview.net
Phenotypical predictors of pregnancy-related restless legs syndrome and their association with basal ganglia and the limbic circuits
Natalia Chechko
Jeremy Lefort-Besnard
Tamme W. Goecke
Markus Frensch
Patricia Schnakenberg
Susanne Stickel
Danilo Bzdok
Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 … (voir plus)postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.
2021-05-10
Scientific Reports (publié)
doi.org
Graph Attention Networks with Positional Embeddings
Liheng Ma
Reihaneh Rabbany
Adriana Romero
2021-05-08
Advances in Knowledge Discovery and Data Mining (publié)
doi.org
arxiv.org
SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks
Farimah Poursafaei
Reihaneh Rabbany
Željko Žilić
2021-05-08
Advances in Knowledge Discovery and Data Mining (publié)
doi.org
Facilitating Asynchronous Participatory Design of Open Source Software: Bringing End Users into the Loop
Jazlyn Hellman
Jinghui Cheng
Jin L.C. Guo
2021-05-07
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (publié)
doi.org
arxiv.org
Interprofessional collaboration and health policy: results from a Quebec mixed method legal research
Marie-Andree Girard
Catherine Régis
Jean-Louis Denis
2021-05-05
Journal of Interprofessional Care (publié)
doi.org
Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders
Sophie-Camille Hogue
Flora Chen
Geneviève Brassard
Denis Lebel
Jean-François Bussières
Audrey Durand
Maxime Thibault
2021-05-05
J. Am. Medical Informatics Assoc. (publié)
doi.org
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
Olivier Vincent
Charley Gros
Julien Cohen-Adad
While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentatio… (voir plus)n tasks, little is known about the impact of individual rater style. This study quantifies rater style in the form of bias and consistency and explores their impacts when used to train deep learning models. Two multi-rater public datasets were used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation (
2021-05-04
ArXiv (prépublication)
arxiv.org
arxiv.org
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks
Olivier Tessier-Larivière
Luke Y. Prince
Pascal Fortier-Poisson
Lorenz Wernisch
Oliver Armitage
Emil Hewage
Guillaume Lajoie
Blake Aaron Richards
Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (voir plus)nown ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
2021-05-03
International IEEE/EMBS Conference on Neural Engineering (publié)
doi.org
Reinforcement Learning with Random Delays
Yann Bouteiller
Simon Ramstedt
Giovanni Beltrame
Christopher Pal
Jonathan Binas
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the ana… (voir plus)tomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this principle to derive Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor-Critic with significantly better performance in environments with delays. This is shown theoretically and also demonstrated practically on a delay-augmented version of the MuJoCo continuous control benchmark.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Associations Between Relative Morning Blood Pressure, Cerebral Blood Flow, and Memory in Older Adults Treated and Controlled for Hypertension
Adrián Noriega de la Colina
Atef Badji
Marie-Christine Robitaille-Grou
Christine Gagnon
Tommy Boshkovski
Maxime Lamarre-Cliche
Sven Joubert
Claudine J. Gauthier
Louis Bherer
Julien Cohen-Adad
Hélène Girouard
Supplemental Digital Content is available in the text. Hypertension, elevated morning blood pressure (BP) surges, and circadian BP variabili… (voir plus)ty constitute risk factors for cerebrovascular events. Nevertheless, while evidence indicates that hypertension is associated with cognitive dysfunctions, the link between BP variability and cognitive performance during aging is not clear. The purpose of this study is to determine the interaction between relative morning BP, cerebral blood flow (CBF) levels, and cognitive performance in hypertensive older adults with controlled BP under antihypertensive treatment. Eighty-four participants aged between 60 and 75 years old were separated into normotensive (n=51) and hypertensive (n=33) groups and underwent 24-hour ambulatory BP monitoring. They were also examined for CBF in the gray matter (CBF-GM) by magnetic resonance imaging and 5 cognitive domains: global cognition, working memory, episodic memory, processing speed, and executive functions. There was no difference in cognitive performance and CBF between normotensive and controlled hypertensive participants. Through a sensitivity analysis, we identified that, among relative morning BP variables, the best fit for CBF values in this cohort was the morning-evening difference in BP. The relative morning BP was negatively associated with CBF-GM in these hypertensive older adults only. In turn, CBF-GM levels were negatively associated with working and episodic memory scores in hypertensive older adults. This is the first extended study demonstrating an association between high relative morning BP and lower levels of CBF-GM, including the further impact of CBF-GM levels on the cognitive performance of specific domains in a community-based cohort of older adults with hypertension.
2021-04-30
Hypertension (publié)
doi.org
Robotic Object Manipulation with Full-Trajectory GAN-Based Imitation Learning
Haoxu Wang
David Meger
This paper develops a novel generative imitation learning system capable of capturing the distribution of expert demonstrations in trajector… (voir plus)y space, which allows longer temporal context within complex motion sequences to be captured. While auto-regressive models that model time-steps sequentially can in principle be recursively applied to capture long sequences, there are known issues with learning such models reliably. In contrast, our model represents full trajectories a first-class entities, which has required us to adapt the typical generative adversarial learning architecture. We pair a full-trajectory discriminator with an imitation-inspired generative trajectory model and train these two in adversarial fashion. Our results show that our method matches the performance of existing approaches for simple tasks, in simulation and on real robot deployments. We produce state-of-the-art accuracy in replicating motions that contain long-term dependencies such as pouring.
2021-04-30
Canadian Conference on Computer and Robot Vision (publié)
doi.org