65 citations · 160 across the 35 of their papers we have counts for
40 papers
Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata +2
Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently…
Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study
Pierre-Louis Benveniste, Julian McGinnis, Shannon Kolind +11
Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consistent instance-level correspondences acr…
A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
Florence Dell'Aniello Picard, Pranav Poudel, Nairouz Shehata +2
Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections disca…
Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking
Dexter Wen Jie Teo, Nairouz Shehata, Herve Lombaert
Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural chang…
Deep Spectral Models for Robust Dental Shape Generation
Tibor Kubík, François Guibault, Michal Španěl +1
Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. However, datasets suitable for training such…
Exploring Entropy-based Active Learning for Fair Brain Segmentation
Ghazal Danaee, Mélanie Gaillochet, Christian Desrosiers +2
Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard uncertainty-based AL methods…
Anatomically-aware conformal prediction for medical image segmentation with random walks
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Con…
TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses
Sahar Dastani, Ali Bahri, Gustavo Adolfo Vargas Hakim +7
State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architecture designed for vision tasks. How…
Prompt learning with bounding box constraints for medical image segmentation
Mélanie Gaillochet, Mehrdad Noori, Sahar Dastani +2
Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised approaches based on bounding box annotation…
ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings
Tibor Kubík, François Guibault, Michal Španěl +1
We introduce ToothForge, a spectral approach for automatically generating novel 3D teeth, effectively addressing the sparsity of dental shape datasets. By operating in the spectral…
Spectral State Space Model for Rotation-Invariant Visual Representation Learning
Sahar Dastani, Ali Bahri, Moslem Yazdanpanah +8
State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity.…
Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Foundation models such as the recently introduced Segment Anything Model (SAM) have achieved remarkable results in image segmentation tasks. However, these models typically require…
Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis
Zeinab Abboud, Herve Lombaert, Samuel Kadoury
Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive uncertainty, they require substantial co…
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Balamurali Murugesan, Sukesh Adiga Vasudeva, Bingyuan Liu +3
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcar…
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to…
Trust your neighbours: Penalty-based constraints for model calibration
Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu +3
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calib…
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing w…
Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning
Mohammad Karami, Hervé Lombaert, David Rivest-Hénault
Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in…
Active learning for medical image segmentation with stochastic batches
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image…
Attention-based Dynamic Subspace Learners for Medical Image Analysis
Sukesh Adiga, Jose Dolz, Herve Lombaert
Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existin…
Test-Time Adaptation with Shape Moments for Image Segmentation
Mathilde Bateson, Hervé Lombaert, Ismail Ben Ayed
Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is…
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is…
Medial Spectral Coordinates for 3D Shape Analysis
Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar +4
In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface po…
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
Benoit Anctil-Robitaille, Antoine Théberge, Pierre-Marc Jodoin +3
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than tho…