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
2025-09-17
ArXiv (prépublication)
arxiv.org
arxiv.org
GROOD: Gradient-Aware Out-of-Distribution Detection
Mostafa ElAraby
Sabyasachi Sahoo
Yann Batiste Pequignot
Paul Novello
Liam Paull
2025-09-16
TMLR (accepté)
openreview.net
openreview.net
On the compatibility of generative AI and generative linguistics
Eva Portelance
Masoud Jasbi
2025-09-16
Nature Computational Science (publié)
doi.org
arxiv.org
Why all roads don't lead to Rome: Representation geometry varies across the human visual cortical hierarchy
Arna Ghosh
Z. Chorghay
Shahab Bakhtiari
Blake Richards
2025-09-16
ArXiv (prépublication)
arxiv.org
arxiv.org
caskade: building Pythonic scientific simulators
Connor Stone
Alexandre Adam
Adam Coogan
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-09-15
Journal of Open Source Software (publié)
doi.org
Low Compute Unlearning via Sparse Representations
Vedant Shah
Frederik Träuble
Ashish Malik
Hugo Larochelle
Michael Curtis Mozer
Sanjeev Arora
Yoshua Bengio
Anirudh Goyal
Machine unlearning, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible … (voir plus)using existing techniques. We propose a low-compute unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the dataset. We evaluate the proposed technique on the problem of class unlearning using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all four datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.
2025-09-15
TMLR (accepté)
openreview.net
openreview.net
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
2025-09-15
TMLR (accepté)
openreview.net
openreview.net
Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses
Emily Kaczmarek
Justin Szeto
Brennan Nichyporuk
Tal Arbel
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.
2025-09-12
ArXiv (prépublication)
arxiv.org
arxiv.org
SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets
Emily Kaczmarek
Justin Szeto
Brennan Nichyporuk
Tal Arbel
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.
2025-09-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Fused Lasso Improves Accuracy of Co-occurrence Network Inference in Grouped Samples
Daniel Agyapong
Briana H. Beatty
Peter G. Kennedy
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.
2025-09-11
ArXiv (prépublication)
arxiv.org
arxiv.org
OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection
Victor Livernoche
Akshatha Arodi
Andreea Musulan
Zachary Yang
Adam Salvail
Ga'etan Marceau Caron
Jean-François Godbout
Reihaneh Rabbany
Deepfakes, synthetic media created using advanced AI techniques, have intensified the spread of misinformation, particularly in politically … (voir plus)sensitive contexts. Existing deepfake detection datasets are often limited, relying on outdated generation methods, low realism, or single-face imagery, restricting the effectiveness for general synthetic image detection. By analyzing social media posts, we identify multiple modalities through which deepfakes propagate misinformation. Furthermore, our human perception study demonstrates that recently developed proprietary models produce synthetic images increasingly indistinguishable from real ones, complicating accurate identification by the general public. Consequently, we present a comprehensive, politically-focused dataset specifically crafted for benchmarking detection against modern generative models. This dataset contains three million real images paired with descriptive captions, which are used for generating 963k corresponding high-quality synthetic images from a mix of proprietary and open-source models. Recognizing the continual evolution of generative techniques, we introduce an innovative crowdsourced adversarial platform, where participants are incentivized to generate and submit challenging synthetic images. This ongoing community-driven initiative ensures that deepfake detection methods remain robust and adaptive, proactively safeguarding public discourse from sophisticated misinformation threats.
2025-09-11
ArXiv (prépublication)
arxiv.org
arxiv.org
FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
Moses Openja
Paolo Arcaini
Foutse Khomh
Fuyuki Ishikawa
2025-09-10
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org