Publications

RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts

Katerina Krejci

Jiri Chmelik

Sandrine B'edard

Falk Eippert

Ulrike Horn

Virginie Callot

Julien Cohen-Adad

Jan Valosek

Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (voir plus)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years

2025-09-16

ArXiv (prépublication)

arxiv.org

caskade: building Pythonic scientific simulators

Connor Stone

Alexandre Adam

Adam Coogan

Laurence Perreault-Levasseur

Yashar Hezaveh

2025-09-14

Journal of Open Source Software (publié)

doi.org

Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training

Hiroki Naganuma

Xinzhi Zhang

Man-Chung Yue

Ioannis Mitliagkas

Russell J. Hewett

Philipp Andre Witte

Yin Tat Lee

2025-09-14

TMLR (accepté)

openreview.net

RetINaBox: A hands-on learning tool for experimental neuroscience

Brune Bettler

Flavia Arias Armas

Erica Cianfarano

Vanessa Bordonaro

Megan Liu

Matthew Loukine

Mingyu Wan

Aude Villemain

Blake Richards

Stuart Trenholm

An exciting aspect of neuroscience is developing and testing hypotheses via experimentation. However, due to logistical and financial hurdle… (voir plus)s, the experiment and discovery component of neuroscience is generally lacking in classroom and outreach settings. To address this issue, here we introduce RetINaBox: a low-cost open-source electronic visual system simulator that provides users with a hands-on tool to discover how the visual system builds feature detectors. RetINaBox features an LED array for generating visual stimuli and a photodiode array that acts as a mosaic of model photoreceptors. Custom software on a Raspberry Pi computer reads out responses from model photoreceptors and allows users to control the polarity and delay of the signal transfer from model photoreceptors to model retinal ganglion cells. Interactive lesson plans are provided, guiding users to discover different types of visual feature detectors—including ON/OFF, center-surround, orientation selective, and direction selective receptive fields—as well as their underlying circuit computations.

2025-09-12

bioRxiv (prépublication)

doi.org

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurologica… (voir plus)l conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer's disease. We use publicly available code and data, and release our trained model at https://github.com/emilykaczmarek/3D-Neuro-SimCLR, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.

2025-09-11

ArXiv (prépublication)

doi.org

arxiv.org

SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets

Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensi… (voir plus)ve research in applying deep learning models to Alzheimer's prediction tasks, these models remain limited by lack of available labeled data, poor generalization across datasets, and inflexibility to varying numbers of input scans and time intervals between scans. In this study, we adapt three state-of-the-art temporal self-supervised learning (SSL) approaches for 3D brain MRI analysis, and add novel extensions designed to handle variable-length inputs and learn robust spatial features. We aggregate four publicly available datasets comprising 3,161 patients for pre-training, and show the performance of our model across multiple Alzheimer's prediction tasks including diagnosis classification, conversion detection, and future conversion prediction. Importantly, our SSL model implemented with temporal order prediction and contrastive learning outperforms supervised learning on six out of seven downstream tasks. It demonstrates adaptability and generalizability across tasks and number of input images with varying time intervals, highlighting its capacity for robust performance across clinical applications. We release our code and model publicly at https://github.com/emilykaczmarek/SSL-AD.

2025-09-11

ArXiv (prépublication)

doi.org

arxiv.org

Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples

Daniel Agyapong

Briana H. Beatty

Peter G. Kennedy

Jane C. Marks

Toby Dylan Hocking

Co-occurrence network inference algorithms have significantly advanced our understanding of microbiome communities. However, these algorithm… (voir plus)s typically analyze microbial associations within samples collected from a single environmental niche, often capturing only static snapshots rather than dynamic microbial processes. Previous studies have commonly grouped samples from different environmental niches together without fully considering how microbial communities adapt their associations when faced with varying ecological conditions. Our study addresses this limitation by explicitly investigating both spatial and temporal dynamics of microbial communities. We analyzed publicly available microbiome abundance data across multiple locations and time points, to evaluate algorithm performance in predicting microbial associations using our proposed Same-All Cross-validation (SAC) framework. SAC evaluates algorithms in two distinct scenarios: training and testing within the same environmental niche (Same), and training and testing on combined data from multiple environmental niches (All). To overcome the limitations of conventional algorithms, we propose fuser, an algorithm that, while not entirely new in machine learning, is novel for microbiome community network inference. It retains subsample-specific signals while simultaneously sharing relevant information across environments during training. Unlike standard approaches that infer a single generalized network from combined data, fuser generates distinct, environment-specific predictive networks. Our results demonstrate that fuser achieves comparable predictive performance to existing algorithms such as glmnet when evaluated within homogeneous environments (Same), and notably reduces test error compared to baseline algorithms in cross-environment (All) scenarios.

2025-09-10

ArXiv (prépublication)

doi.org

arxiv.org

Illusions of AI consciousness

Yoshua Bengio

Eric Elmoznino

The belief that AI is conscious is not without risk

2025-09-10

Science (publié)

doi.org

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks

Moses Openja

Paolo Arcaini

Foutse Khomh

Fuyuki Ishikawa

2025-09-09

ACM Transactions on Software Engineering and Methodology (publié)

doi.org

arxiv.org

EZH2 Inhibition Induces an Integrated Stress Response Driving Glutamine-Dependent Vulnerability in TNBC

Lucas Porras

Marina Fukano

Ann-Sophie Gironne

Elise Quadri

Gabriel Alzial

Hugo Philippeau

Yousef Aleassa

Anie Monast

Faustine Gorse

Myriame Saint-Arnaud

Mariana De Sa Tavares Russo

Sylvie Mader

Daina Avizonis

Sébastien Lemieux

Morag Park

Geneviève Deblois

EZH2, the catalytic subunit of Polycomb Repressive Complex II, is highly expressed and associated with poor prognosis in triple-negative bre… (voir plus)ast cancer (TNBC). Despite inducing significant changes in chromatin profiles and gene expression, EZH2 inhibition in TNBC models has limited impact on growth, suggesting adaptive compensatory mechanisms. Here, we demonstrate that EZH2 inhibition induces accumulation of double-stranded RNA and misfolded proteins in TNBC, activating an integrated stress response (ISR) via the PKR/PERK-eIF2α pathway. We identify Activating Transcription Factor 4 (ATF4) as a key effector upon EZH2 inhibition, driving metabolic changes characterized by increased amino acid uptake and glutamine dependency. Targeting this ISR-ATF4-mediated metabolic response using glutaminase inhibitor in combination with EZH2 inhibition significantly impairs TNBC cell proliferation and tumor progression. These findings reveal a stress-driven metabolic adaptation that enables TNBC survival upon EZH2 blockade, highlighting inhibition of this pathway as a strategy to enhance the efficacy of EZH2 inhibitors in TNBC.

2025-09-08

bioRxiv (prépublication)

doi.org

An AI system to help scientists write expert-level empirical software

Eser Aygün

Anastasiya Belyaeva

Gheorghe Comanici

Marc Coram

Hao Cui

Jake Garrison

Renee Johnston Anton Kast

Cory Y. McLean

Peter C. Norgaard

Zahra Shamsi

David Smalling

James Thompson

Subhashini Venugopalan

Brian P Williams

Chujun He

Sarah Martinson

Martyna Plomecka

Lai Wei

Yuchen Zhou

Qian-Ze Zhu … (voir 21 de plus)

Matthew Abraham

Erica Brand

Anna Bulanova

Jeffrey A. Cardille

Chris Co

Scott Ellsworth

Grace Joseph

Malcolm Kane

Ryan K. Krueger

Johan Kartiwa

D. Liebling

Jan-Matthis Lueckmann

Paul Raccuglia

Xuefei Wang

Katherine Chou

James Manyika

Yossi Matias

J.C. Platt

Lizzie Dorfman

Shibl Mourad

Michael P. Brenner

The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. … (voir plus)To address this, we present an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. The system achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a wide range of benchmarks. In bioinformatics, it discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, it generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. Our method also produced state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. By devising and implementing novel solutions to diverse tasks, the system represents a significant step towards accelerating scientific progress.

2025-09-07

ArXiv (prépublication)

doi.org

arxiv.org

Task Robustness via Re-Labelling Vision-Action Robot Data

Artur Kuramshin

Özgür Aslan

Cyrus Neary

Glen Berseth

2025-09-05

robot-learning.org/CoRL/2025/Workshop/Robot_Data (publié)

openreview.net