Publications

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
2022-04-08
Scientific Reports (publié)
doi.org
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Reza Azad
Moein Heidari
Julien Cohen-Adad
Ehsan Adeli
Dorit Merhof
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related disea… (voir plus)ses such as osteoporosis, vertebral fractures, and intervertebral disc herniation. To date, various approaches have been developed in the literature which routinely relies on detecting the discs as the primary step. A disadvantage of many cohort studies is that the localization algorithm also yields false-positive detections. In this study, we aim to alleviate this problem by proposing a novel U-Net-based structure to predict a set of candidates for intervertebral disc locations. In our design, we integrate the image shape information (image gradients) to encourage the model to learn rich and generic geometrical information. This additional signal guides the model to selectively emphasize the contextual representation and suppress the less discriminative features. On the post-processing side, to further decrease the false positive rate, we propose a permutation invariant 'look once' model, which accelerates the candidate recovery procedure. In comparison with previous studies, our proposed approach does not need to perform the selection in an iterative fashion. The proposed method was evaluated on the spine generic public multi-center dataset and demonstrated superior performance compared to previous work. We have provided the implementation code in https://github.com/rezazad68/intervertebral-lookonce
2022-04-06
ArXiv (preprint)
doi.org
arxiv.org
Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features
Alexandre Lachance
Mathieu Godbout
Fares Antaki
Mélanie Hébert
Serge Bourgault
Mathieu Caissie
Éric Tourville
Audrey Durand
Ali Dirani
2022-04-06
Translational Vision Science & Technology (publié)
doi.org
Structure-aware protein self-supervised learning
Can Chen
Jingbo Zhou
Fan Wang
Xue (Steve) Liu
Dejing Dou
Abstract Motivation Protein representation learning methods have shown great potential to many downstream tasks in biological applications. … (voir plus)A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information. Results In this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method. Availability and implementation The Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.
2022-04-06
ArXiv (preprint)
doi.org
arxiv.org
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Zichao Li
Prakhar Sharma
Xing Han Lu
Jackie Cheung
Siva Reddy
Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.In this paper, we a… (voir plus)sk the question: Can we improve QA systems further post-deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system’s performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.The feedback contains both structured ratings and unstructured natural language explanations.We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers.
2022-04-06
ArXiv (preprint)
doi.org
arxiv.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
M. M. Laganá
Francesca Baglio
S. Vannesjo
Haleh Karbasforoushan
Maryam Seif
A. Seifert
Junqian Xu
Joo-won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca Sara Samson
Francesco Grussu
Marco Battiston
C. G. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (voir 4 de plus)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (voir plus) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (published)
doi.org
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen-Adad
Eva Alonso‐Ortiz
Stephanie Alley
Maria Marcella Lagana
Francesca Baglio
Signe Johanna Vannesjo
Haleh Karbasforoushan
Maryam Seif
Alan C. Seifert
Junqian Xu
Joo‐Won Kim
René Labounek
Lubomír Vojtíšek
Marek Dostál
Jan Valošek
Rebecca S. Samson
Francesco Grussu
Marco Battiston
Claudia A. M. Gandini Wheeler-Kingshott
Marios C. Yiannakas … (voir 4 de plus)
Guillaume Gilbert
Torben Schneider
Brian Johnson
Ferran Prados
Spinal cord gray‐matter imaging is valuable for a number of applications, but remains challenging. The purpose of this work was to compare… (voir plus) various MRI protocols at 1.5 T, 3 T, and 7 T for visualizing the gray matter.
2022-04-05
Magnetic Resonance in Medicine (publié)
doi.org
E VALUATING G ENERALIZATION IN GF LOW N ETS FOR M OLECULE D ESIGN
Andrei Cristian Nica
Moksh J. Jain
Emmanuel Bengio
Cheng-Hao Liu
Maksym Korablyov
Michael M. Bronstein
Yoshua Bengio
Deep learning bears promise for drug discovery problems such as de novo molecular design. Generating data to train such models is a costly a… (voir plus)nd time-consuming process, given the need for wet-lab experiments or expensive simulations. This problem is compounded by the notorious data-hungriness of machine learning algorithms. In small molecule generation the recently proposed GFlowNet method has shown good performance in generating diverse high-scoring candidates, and has the interesting advantage of being an off-policy offline method. Finding an appropriate generalization evaluation metric for such models, one predictive of the desired search performance (i.e. finding high-scoring diverse candidates), will help guide online data collection for such an algorithm. In this work, we develop techniques for evaluating GFlowNet performance on a test set, and identify the most promising metric for predicting generalization. We present empirical results on several small-molecule design tasks in drug discovery, for several GFlowNet training setups, and we find a metric strongly correlated with diverse high-scoring batch generation. This metric should be used to identify the best generative model from which to sample batches of molecules to be evaluated.
2022-04-05
ICLR.cc/2022/Workshop/MLDD (poster)
openreview.net
openreview.net
Naming Autism in the Right Context.
Andres Roman-Urrestarazu
Guillaume Dumas
Varun Warrier
2022-04-04
JAMA pediatrics (published)
doi.org
Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning
Daniel Gourdeau
Olivier Potvin
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Diego Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Yang
Nathalie Duchesne
Simon Duchesne
2022-04-04
Scientific Reports (publié)
doi.org
TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING
Daniel Gourdeau
Olivier Potvin
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Diego Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Yang
Nathalie Duchesne
Simon Duchesne
2022-04-04
Scientific Reports (publié)
doi.org
Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning
Daniel Gourdeau
Olivier Potvin
Patrick Archambault
Carl Chartrand‐lefebvre
Louis Dieumegarde
Reza Forghani
Christian Gagné
Alexandre Hains
David Hornstein
Huy Khiem Le
Simon Lemieux
Marie‐hélène Lévesque
Diego R. Martin
Lorne Rosenbloom
An Tang
Fabrizio Vecchio
Issac Y Yang
N. Duchesne
Simon Duchesne
2022-04-04
Scientific Reports (published)
doi.org