Publications

Defining Feasibility as a Criterion for Essential Surgery: A Qualitative Study with Global Children’s Surgery Experts
Alizeh Abbas
Henry E. Rice
Dan Poenaru
Lubna Samad
2023-10-13
World Journal of Surgery (publié)
doi.org
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Florence Regol
Joud Chataoui
Mark J. Coates
2023-10-12
ArXiv (prépublication)
doi.org
arxiv.org
Predicting Solar PV Output Based on Hybrid Deep Learning and Physical
Models: Case Study of Morocco
Samira Abousaid
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelaziz Berrado
Hicham Bouzekri
2023-10-12
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) (publié)
doi.org
Prognosis of critically ill immunocompromised patients with virus-detected acute respiratory failure
Guillaume Dumas
Maxime Bertrand
Virginie Lemiale
Emmanuel Canet
François Barbier
Achille Kouatchet
Alexandre Demoule
Kada Klouche
Anne-Sophie Moreau
Laurent Argaud
Florent Wallet
Jean Herlé Raphalen
Djamel Mokart
Fabrice Bruneel
Frédéric Pène
Elie Azoulay
2023-10-12
Annals of Intensive Care (publié)
doi.org
Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)
Matteo Biagiola
Nicolás Cardozo
Donghwan Shin
Foutse Khomh
Andrea Stocco
Vincenzo Riccio
2023-10-12
ACM SIGSOFT Softw. Eng. Notes (publié)
doi.org
A cry for help: Early detection of brain injury in newborns
Charles Onu
Samantha Latremouille
Arsenii Gorin
Junhao Wang
Uchenna Ekwochi
P. Ubuane
O. Kehinde
Muhammad A. Salisu
Datonye Briggs
Yoshua Bengio
Doina Precup
2023-10-11
ArXiv (prépublication)
doi.org
arxiv.org
AAPM Medical Physics Practice Guideline 14.a: Yttrium‐90 microsphere radioembolization
Nathan C. Busse
Muthana S. A. L. Al‐Ghazi
Nadine Abi‐Jaoudeh
Diane Alvarez
Ahmet S. Ayan
Erli Chen
Michael D. Chuong
William A. Dezarn
S. Enger
Stephen A. Graves
Robert F. Hobbs
Mary Ellen Jafari
S. Peter Kim
Nichole M. Maughan
Andrew M. Polemi
Jennifer R. Stickel
2023-10-10
Journal of Applied Clinical Medical Physics (publié)
doi.org
A general framework for the practical disintegration of PAC-Bayesian bounds
Paul Viallard
Pascal Germain
Amaury Habrard
Emilie Morvant
2023-10-10
Machine-mediated learning (publié)
doi.org
arxiv.org
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning
Bahareh Nikpour
Narges Armanfard
Attention mechanisms have demonstrated significant potential in enhancing learning models by identifying key portions of input data, particu… (voir plus)larly in scenarios with limited training samples. Inspired by human perception, we propose that focusing on essential data segments, rather than the entire dataset, can improve the accuracy and reliability of the learning models. However, identifying these critical data segments, or"hard attention finding,"is challenging, especially in few-shot learning, due to the scarcity of training data and the complexity of model parameters. To address this, we introduce LaHA, a novel framework that leverages language-guided deep reinforcement learning to identify and utilize informative data regions, thereby improving both interpretability and performance. Extensive experiments on benchmark datasets validate the effectiveness of LaHA.
2023-10-10
ArXiv (prépublication)
doi.org
arxiv.org
A deep learning benchmark for first break detection from hardrock seismic reflection data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
Ernst Schetselaar
Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. Although many works ha… (voir plus)ve shown the benefits of deep learning, assessing the generalization capabilities of proposed methods for data acquired in different conditions and geologic environments remains challenging. This is especially true for applications in hardrock environments. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled data sets and the use of inadequate evaluation methodologies. Because machine learning models are prone to overfit and underperform when the data used to train them are site specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we develop a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey data set acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons and discuss potential improvements to this approach.
2023-10-09
Geophysics (publié)
doi.org
Large Language Models can Learn Rules
Zhaocheng Zhu
Yuan Xue
Xinyun Chen
Denny Zhou
Jian Tang
Dale Schuurmans
Hanjun Dai
2023-10-09
ArXiv (prépublication)
doi.org
openreview.net
Debiasing Counterfactuals in the Presence of Spurious Correlations
Amar Kumar
Nima Fathi
Raghav Mehta
Brennan Nichyporuk
Jean-Pierre R. Falet
Sotirios A. Tsaftaris
Tal Arbel
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correl… (voir plus)ations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.
2023-10-08
Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (publié)
doi.org
openreview.net