Publications

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
Marie-Hélène Bourget
Lee Kamentsky
Satrajit S. Ghosh
Giacomo Mazzamuto
Alberto Lazari
Christopher J. Markiewicz
Robert Oostenveld
Guiomar Niso
Yaroslav O. Halchenko
Ilona Lipp
Sylvain Takerkart
Paule-Joanne Toussaint
Ali R. Khan
Gustav Nilsonne
Filippo Maria Castelli
Stefan Ross Eric Franklin Anthony Rémi Christopher J. Taylor Appelhoff
Julien Cohen-Adad
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusab… (voir plus)le way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI.
2022-04-18
Frontiers in Neuroscience (publié)
doi.org
Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran
Mathilde Caron
Ishan Misra
Piotr Bojanowski
Florian Bordes
P Vincent
Armand Joulin
Michael G. Rabbat
Nicolas Ballas
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the … (voir plus)representation of an image view containing randomly masked patches to the representation of the original unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark. Our code is publicly available.
2022-04-13
ArXiv (prépublication)
doi.org
arxiv.org
Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations
Anthony Bilodeau
Constantin V.L. Delmas
Martin Parent
Paul De Koninck
Audrey Durand
Flavie Lavoie-Cardinal
2022-04-13
Nature Machine Intelligence (publié)
doi.org
PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information
Faizy Ahsan
Zichao Yan
Doina Precup
Mathieu Blanchette
Supplementary data are available at Bioinformatics online.
2022-04-13
Bioinformatics (publié)
doi.org
User Experience of a Computer-Based Decision Aid for Prenatal Trisomy Screening: Mixed Methods Explanatory Study
Titilayo Tatiana Agbadje
Chantale Pilon
Pierre Bérubé
Jean-Claude Forest
François Rousseau
S. A. Rahimi
Yves Giguère
France Légaré
2022-04-13
JMIR Pediatrics and Parenting (publié)
doi.org
Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients
Daniel Gourdeau
Olivier Potvin
Jason Henry Biem
Florence Cloutier
Lyna Abrougui
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Christian Gagné
Louis Gagnon
Raphaelle Giguère
Alexandre Hains
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Simon Nepveu
Lorne Rosenbloom
An Tang
Issac Yang
Nathalie Duchesne … (voir 1 de plus)
Simon Duchesne
2022-04-12
Scientific Reports (publié)
doi.org
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
Vaibhav Adlakha
Shehzaad Dhuliawala
Kaheer Suleman
Harm de Vries
Siva Reddy
2022-04-12
Transactions of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
A neurodynamic model of inter-brain coupling in the gamma band
Quentin Moreau
Lena Adel
Caitriona Douglas
Ranjbaran Ghazaleh
Guillaume Dumas
2022-04-10
bioRxiv (prépublication)
doi.org
Can neurogenesis act as a neural regularizer?
Lina M. Tran
Adam Santoro
Lulu Liu
Sheena A. Josselyn
Blake A. Richards
Paul W. Frankland
New neurons are continuously generated in the subgranular zone of the dentate gyrus throughout adulthood. These new neurons gradually integr… (voir plus)ate into hippocampal circuits, forming new naïve synapses. Viewed from this perspective, these new neurons may represent a significant source of ‘wiring’ noise in hippocampal networks. In machine learning, such noise injection is commonly used as a regularization technique. Regularization techniques help prevent overfitting training data, and allow models to generalize learning to new, unseen data. Using a computational modeling approach, here we ask whether a neurogenesis-like process similarly acts as a regularizer, facilitating generalization in a category learning task. In a convolutional neural network (CNN) trained on the CIFAR-10 object recognition dataset, we modeled neurogenesis as a replacement/turnover mechanism, where weights for a randomly chosen small subset of neurons in a chosen hidden layer were re-initialized to new values as the model learned to categorize 10 different classes of objects. We found that neurogenesis enhanced generalization on unseen test data compared to networks with no neurogenesis. Moreover, neurogenic networks either outperformed or performed similarly to networks with conventional noise injection (i.e., dropout, weight decay, and neural noise). These results suggest that neurogenesis can enhance generalization in hippocampal learning through noise-injection, expanding on the roles that neurogenesis may have in cognition. In deep neural networks, various forms of noise injection are used as regularization techniques to prevent overfitting and promote generalization on unseen test data. Here, we were interested in whether adult neurogenesis– the lifelong production of new neurons in the hippocampus– might similarly function as a regularizer in the brain. We explored this question computationally, assessing whether implementing a neurogenesis-like process in a hidden layer within a convolutional neural network trained in a category learning task would prevent overfitting and promote generalization. We found that neurogenesis regularization was as least as effective as, or more effective than, conventional regularizers (i.e., dropout, weight decay and neural noise) in improving model performance. These results suggest that optimal levels of hippocampal neurogenesis may improve memory-guided decision making by preventing overfitting, thereby promoting the formation of more generalized memories that can be applied in a broader range of circumstances. We outline how these predictions may be evaluated behaviorally in rodents with altered hippocampal neurogenesis.
2022-04-09
bioRxiv (prépublication)
doi.org
GCNFusion: An efficient graph convolutional network based model for information diffusion
Bahareh Fatemi
Soheila Molaei
Shirui Pan
Samira Abbasgholizadeh Rahimi
2022-04-08
Expert Systems with Applications (inconnu)
doi.org
Clones in Deep Learning Code: What, Where, and Why?
Hadhemi Jebnoun
Md Saidur Rahman
Foutse Khomh
Biruk Asmare Muse
Deep Learning applications are becoming increasingly popular. Developers of deep learning systems strive to write more efficient code. Deep … (voir plus)learning systems are constantly evolving, imposing tighter development timelines and increasing complexity, which may lead to bad design decisions. A copy-paste approach is widely used among deep learning developers because they rely on common frameworks and duplicate similar tasks. Developers often fail to properly propagate changes to all clones fragments during a maintenance activity. To our knowledge, no study has examined code cloning practices in deep learning development. Given the negative impacts of clones on software quality reported in the studies on traditional systems, it is very important to understand the characteristics and potential impacts of code clones on deep learning systems. To this end, we use the NiCad tool to detect clones from 59 Python, 14 C# and 6 Java-based deep learning systems and an equal number of traditional software systems. We then analyze the frequency and distribution of code clones in deep learning and traditional systems. We do further analysis of the distribution of code clones using location-based taxonomy. We also study the correlation between bugs and code clones to assess the impacts of clones on the quality of the studied systems. Finally, we introduce a code clone taxonomy related to deep learning programs and identify the deep learning system development phases in which cloning has the highest risk of faults. Our results show that code cloning is a frequent practice in deep learning systems and that deep learning developers often clone code from files in distant repositories in the system. In addition, we found that code cloning occurs more frequently during DL model construction. And that hyperparameters setting is the phase during which cloning is the riskiest, since it often leads to faults.
2022-04-07
Empirical Software Engineering (publié)
doi.org
arxiv.org
Rapid, automated nerve histomorphometry through open-source artificial intelligence
Simeon Christian Daeschler
Marie-Hélène Bourget
Dorsa Derakhshan
Vasudev Sharma
Stoyan Ivaylov Asenov
Tessa Gordon
Julien Cohen-Adad
Gregory Howard Borschel
We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers… (voir plus) from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was trained for automated axon/myelin segmentation using a dataset of light-microscopic cross-sectional images of osmium tetroxide-stained rat nerves including various axonal regeneration stages. In a second dataset, accuracy of automated segmentation was determined against manual axon/myelin labels. Automated morphometry results, including axon diameter, myelin sheath thickness and g-ratio were compared against manual straight-line measurements and morphometrics extracted from manual labels with AxonDeepSeg as a reference standard. The neural network achieved high pixel-wise accuracy for nerve fiber segmentations with a mean (± standard deviation) ground truth overlap of 0.93 (± 0.03) for axons and 0.99 (± 0.01) for myelin sheaths, respectively. Nerve fibers were identified with a sensitivity of 0.99 and a precision of 0.97. For each nerve fiber, the myelin thickness, axon diameter, g-ratio, solidity, eccentricity, orientation, and individual x -and y-coordinates were determined automatically. Compared to manual morphometry, automated histomorphometry showed superior agreement with the reference standard while reducing the analysis time to below 2.5% of the time needed for manual morphometry. This open-source convolutional neural network provides rapid and accurate morphometry of entire peripheral nerve cross-sections. Given its easy applicability, it could contribute to significant time savings in biomedical research while extracting unprecedented amounts of objective morphologic information from large image datasets.
2022-04-07
Scientific Reports (publié)
doi.org