Hervé Lombaert

PhD - Polytechnique Montréal

PhD - Polytechnique Montréal

Nairouz Shehata

Postdoctorate - Polytechnique Montréal

Google Scholar

Matei Voillemier

Master's Research - Polytechnique Montréal

Publications

Exploring Entropy-based Active Learning for Fair Brain Segmentation

Ghazal Danaee

Christian Desrosiers

Sylvain Bouix

Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. Howeve… (see more)r, standard uncertainty-based AL methods typically focus on maximizing performance metrics, ignoring performance disparities or fairness across groups with sensitive attributes. While fair active learning has been explored in classification tasks, its intersection with medical image segmentation remains unaddressed. In this work, we introduced a fairness-aware active learning framework with a Weighted Entropy selection strategy that modulates uncertainty based on current group-specific performance estimates on the labeled set. To decouple true epistemic uncertainty from anatomical volume variances, we further utilized a masked, scaled entropy restricted to the region of interest. The framework was evaluated on synthetic T1-weighted brain MRIs with controlled left caudate bias in both strong and weak bias settings. A 3D U-Net was trained to segment the left caudate under several AL strategies, starting from both demographically balanced and strongly imbalanced initial labeled sets. Experiments demonstrated that our method markedly reduces performance disparities between groups compared to random sampling and standard uncertainty sampling. By prioritizing poorly segmented subgroups during the AL cycles, our method consistently achieved the highest equity-scaled performance and reduced the disparity metric by 75% (strong bias) and 86% (weak bias) relative to standard entropy at the final budget. Overall, this work is among the first studies on fair AL for medical image segmentation, offering an efficient strategy to train more equitable models in resource-constrained environments.

2026-05-04

Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers

Pierre‐Louis Benveniste

Laurent Létourneau‐Guillon

David Araújo

Lydia Chougar

Dumitru Fetco

Masaaki Hori

Kouhei Kamiya

Steven Messina

Charidimos Tsagkas

Bertrand Audoin

Rohit Bakshi

Élise Bannier

Daniel Blezek

Jean‐Christophe Brisset

Virginie Callot

Erik Charlson

Michelle Chen

Olga Ciccarelli

Sarah Demortière

Gilles Edan … (see 36 more)

M Filippi

Tobias Granberg

Cristina Granziera

Christopher C. Hemond

B. Mark Keegan

Anne Kerbrat

J Kirschke

Petr Kudlička

Pierre Labauge

Lisa Eunyoung Lee

Yaou Liu

Caterina Mainero

Julian McGinnis

Nilser Laines Medina

Mark Mühlau

Govind Nair

Kristin P. O’Grady

Jiwon Oh

Russell Ouellette

Alexandre Prat

Daniel S. Reich

Maria A. Rocca

Timothy M. Shepherd

Seth A. Smith

Leszek Stawiarz

Jason Talbott

Roger Tam

Shahamat Tauhid

Anthony Traboulsee

Constantina A. Treaba

Paola Valsasina

Zachary Vavasour

Marios Yiannakas

Julien Cohen-Adad

Shannon Kolind

The proposed model can achieve accurate and reliable spinal cord MS lesion segmentation across heterogeneous MRI data, addressing a key barr… (see more)ier to clinical translation. The model is available in the Spinal Cord Toolbox v7.2 and higher.Code repository: https://github.com/ivadomed/seg-sc-ms-lesion-multicontrast.

2026-04-23

Multiple Sclerosis Journal (published)

Anatomically-aware conformal prediction for medical image segmentation with random walks

Christian Desrosiers

2026-01-25

ArXiv (preprint)

Variational Visible Layers: A Practical Framework for Uncertainty Estimation

Zeinab Abboud

Samuel Kadoury

2025-09-19

Lecture Notes in Computer Science (published)

TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses

Sahar Dastani

Ali Bahri

Gustavo Adolf Vargas Hakim

Moslem Yazdanpanah

Mehrdad Noori

David Osowiechi

Samuel Barbeau

Ismail Ben Ayed

Christian Desrosiers

State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering arc… (see more)hitecture designed for vision tasks. However, their generalization performance degrades significantly under distribution shifts. To address this limitation, we propose TRUST (Test-Time Refinement using Uncertainty-Guided SSM Traverses), a novel test-time adaptation (TTA) method that leverages diverse traversal permutations to generate multiple causal perspectives of the input image. Model predictions serve as pseudo-labels to guide updates of the Mamba-specific parameters, and the adapted weights are averaged to integrate the learned information across traversal scans. Altogether, TRUST is the first approach that explicitly leverages the unique architectural properties of SSMs for adaptation. Experiments on seven benchmarks show that TRUST consistently improves robustness and outperforms existing TTA methods.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

openreview.net

Prompt learning with bounding box constraints for medical image segmentation.

Mehrdad Noori

Sahar Dastani

Christian Desrosiers

Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised app… (see more)roaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multi-modal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code will be available upon acceptance

2025-06-23

IEEE transactions on bio-medical engineering (published)

Spectral State Space Model for Rotation-Invariant Visual Representation Learning

Sahar Dastani

Ali Bahri

Moslem Yazdanpanah

Mehrdad Noori

David Osowiechi

Gustavo Adolfo Vargas Hakim

Farzad Beizaee

Milad Cheraghalikhani

Arnab Kumar Mondal

Christian Desrosiers

State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling glob… (see more)al relationships with linear complexity. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to identify relationships between conceptually related yet not adjacent patches. This limitation arises from the non-causal nature of image data, which lacks inherent directional relationships. Additionally, current vision-based SSMs are highly sensitive to transformations such as rotation. Their predefined scanning directions depend on the original image orientation, which can cause the model to produce inconsistent patch-processing sequences after rotation. To address these limitations, we introduce Spectral VMamba, a novel approach that effectively captures the global structure within an image by leveraging spectral information derived from the graph Laplacian of image patches. Through spectral decomposition, our approach encodes patch relationships independently of image orientation, achieving rotation invariance with the aid of our Rotational Feature Normalizer (RFN) module. Our experiments on classification tasks show that Spectral VMamba outperforms the leading SSM models in vision, such as VMamba, while maintaining invariance to rotations and a providing a similar runtime efficiency.

2025-06-09

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings

Tibor Kubík

Franccois Guibault

Michal vSpanvel

We introduce ToothForge, a spectral approach for automatically generating novel 3D teeth, effectively addressing the sparsity of dental shap… (see more)e datasets. By operating in the spectral domain, our method enables compact machine learning modeling, allowing the generation of high-resolution tooth meshes in milliseconds. However, generating shape spectra comes with the instability of the decomposed harmonics. To address this, we propose modeling the latent manifold on synchronized frequential embeddings. Spectra of all data samples are aligned to a common basis prior to the training procedure, effectively eliminating biases introduced by the decomposition instability. Furthermore, synchronized modeling removes the limiting factor imposed by previous methods, which require all shapes to share a common fixed connectivity. Using a private dataset of real dental crowns, we observe a greater reconstruction quality of the synthetized shapes, exceeding those of models trained on unaligned embeddings. We also explore additional applications of spectral analysis in digital dentistry, such as shape compression and interpolation. ToothForge facilitates a range of approaches at the intersection of spectral analysis and machine learning, with fewer restrictions on mesh structure. This makes it applicable for shape analysis not only in dentistry, but also in broader medical applications, where guaranteeing consistent connectivity across shapes from various clinics is unrealistic. The code is available at https://github.com/tiborkubik/toothForge.

2025-06-02

ArXiv (preprint)

GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation

Sukesh Adiga Vasudeva

Jose Dolz

Soft-label assignments have emerged as prominent strategies in training dense prediction problems, such as image segmentation. These approac… (see more)hes mitigate the limitations of hard labels, such as inter-class relationships in the data and spatial relationships between a given pixel and its neighbors. Nevertheless, most existing methods rely only on ground-truth masks and ignore the underlying image context associated with each label. For instance, image intensities convey information that could potentially clear ambiguities in the annotation. This paper, therefore, proposes a Geodesic Label Smoothing (GeoLS) approach that incorporates image intensity information within the soft labeling process. Specifically, we leverage the geodesic distance transform to capture the intensity variations between pixels. The generated maps geodesically modify the hard labels to obtain new intensity-based soft labels. The resulting geodesic soft labels better model spatial and class-wise relationships as they capture the variations of image gradients across classes and anatomy. The benefits of our intensity-based geodesic soft labels are assessed on three diverse sets of publicly accessible segmentation datasets. Our experimental results show that the proposed method consistently improves the segmentation accuracy compared to state-of-the-art soft-labeling techniques in terms of the Dice similarity and Hausdorff distance.

2025-04-17

Machine Learning for Biomedical Imaging (published)

Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading

Tien-en Chang

2024-12-31

ShapeMI@MICCAI (published)

Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis

Zeinab Abboud

Samuel Kadoury

Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive … (see more)uncertainty, they require substantial computational resources to train. Although Bayesian approximations such as ensembles have shown promise, they still suffer from high training and inference costs. Existing approaches mainly address the costs of BNN inference post-training, with little focus on improving training efficiency and reducing parameter complexity. This study introduces a training procedure for a sparse (partial) Bayesian network. Our method selectively assigns a subset of parameters as Bayesian by assessing their deterministic saliency through gradient sensitivity analysis. The resulting network combines deterministic and Bayesian parameters, exploiting the advantages of both representations to achieve high task-specific performance and minimize predictive uncertainty. Demonstrated on multi-label ChestMNIST for classification and ISIC, LIDC-IDRI for segmentation, our approach achieves competitive performance and predictive uncertainty estimation by reducing Bayesian parameters by over 95\%, significantly reducing computational expenses compared to fully Bayesian and ensemble methods.

2024-10-02

Lecture Notes in Computer Science (published)

Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision

Christian Desrosiers

2024-09-27

Lecture Notes in Computer Science (published)