Publications

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (voir plus)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties,...).
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)
doi.org
openreview.net
Segmentation-Consistent Probabilistic Lesion Counting
Julien Schroeter
Chelsea Myers-Colet
Douglas Arnold
Tal Arbel
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (voir plus)ging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)
doi.org
openreview.net
Tackling hypo and hyper sensory processing heterogeneity in autism: From clinical stratification to genetic pathways
Aline Lefebvre
Julian Tillmann
Freddy Cliquet
Frederique Amsellem
Anna Maruani
Claire Leblond
Anita Beggiato
David Germanaud
Anouck Amestoy
Myriam Ly‐Le Moal
Daniel Umbricht
Christopher H. Chatham
Lorraine Murtagh
Manuel Bouvard
Marion Leboyer
Tony Charman
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2022-12-04
Autism Research (publié)
doi.org
Performative Prediction in Time Series: A Case Study
Rupali Bhati
Jennifer Jones
David Langelier
Anthony Reiman
Jonathan Greenland
Kristin Campbell
Audrey Durand
2022-12-02
NeurIPS.cc/2022/Workshop/TS4H (poster)
openreview.net
openreview.net
Active Keyword Selection to Track Evolving Topics on Twitter
Sacha Lévy
Farimah Poursafaei
Kellin Pelrine
Reihaneh Rabbany
How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as econo… (voir plus)mics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers.
2022-12-01
2022 IEEE International Conference on Data Mining Workshops (ICDMW) (publié)
doi.org
arxiv.org
Autism incidence and spatial analysis in more than 7 million pupils in English schools: a retrospective, longitudinal, school registry study.
Andres Roman-Urrestarazu
Justin Christopher Yang
R. van Kessel
Varun Warrier
Guillaume Dumas
H. Jongsma
Gabriel Gatica-bahamonde
Carrie Allison
F. Matthews
Simon Baron-Cohen
C. Brayne
2022-12-01
The Lancet Child & Adolescent Health (publié)
doi.org
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining
Andreas Madsen
Nicholas Meade
Vaibhav Adlakha
Siva Reddy
To explain NLP models a popular approach is to use importance measures, such as attention, which inform input tokens are important for makin… (voir plus)g a prediction. However, an open question is how well these explanations accurately reflect a model's logic, a property called faithfulness. To answer this question, we propose Recursive ROAR, a new faithfulness metric. This works by recursively masking allegedly important tokens and then retraining the model. The principle is that this should result in worse model performance compared to masking random tokens. The result is a performance curve given a masking-ratio. Furthermore, we propose a summarizing metric using relative area-between-curves (RACU), which allows for easy comparison across papers, models, and tasks. We evaluate 4 different importance measures on 8 different datasets, using both LSTM-attention models and RoBERTa models. We find that the faithfulness of importance measures is both model-dependent and task-dependent. This conclusion contradicts previous evaluations in both computer vision and faithfulness of attention literature.
2022-12-01
Findings of the Association for Computational Linguistics: EMNLP 2022 (publié)
doi.org
arxiv.org
Implementing automation in deep brain stimulation: has the time come?
Marco Bonizzato
Alfonso Fasano
2022-12-01
The Lancet Digital Health (publié)
doi.org
Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Singh Sachan
Mike Lewis
Mandar Joshi
Armen Aghajanyan
Wen-tau Yih
Joelle Pineau
Luke Zettlemoyer
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
In-Processing Fairness Improvement Methods for Regression Data-Driven Building Models: Achieving Uniform Energy Prediction
Ying Sun
Benjamin Fung
Fariborz Haghighat
2022-12-01
Energy and Buildings (publié)
doi.org
A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques
Malik H. Altakrori
Thomas Scialom
Benjamin Fung
Jackie Cheung
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang
Bang Liu
Siliang Tang
Lingfei Wu
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input cont… (voir plus)ext of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts. As a result, they may wrongly penalize a legitimate and reasonable candidate question when it (1) involves complicated reasoning with the context or (2) can be grounded by multiple evidences in the context.In this paper, we propose QRelScore, a context-aware Relevance evaluation metric for Question Generation.Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation to cope with the complicated reasoning and diverse generation from multiple evidences, respectively.Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org