Publications

Gregory Howard Borschel

2022-04-08

Scientific Reports (published)

Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach

Reza Azad

Moein Heidari

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… (see more)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)

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 (published)

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. … (see more)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)

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… (see more)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)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

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 … (see 4 more)

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… (see more) 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)

Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter

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 … (see 4 more)

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… (see more) 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)

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… (see more)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

Naming Autism in the Right Context.

Andres Roman-Urrestarazu

Guillaume Dumas

Varun Warrier

2022-04-04

JAMA pediatrics (published)

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)

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 (published)

Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation

Kushal Arora

Layla El Asri

Hareesh Bahuleyan

Jackie Cheung

Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis for … (see more)this brittleness of generation models is that it is caused by the training and the generation procedure mismatch, also referred to as exposure bias. In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective. We show that exposure bias leads to an accumulation of errors during generation, analyze why perplexity fails to capture this accumulation of errors, and empirically show that this accumulation results in poor generation quality.

2022-04-03

ArXiv (preprint)