8 papers
Deep Spectral Models for Robust Dental Shape Generation
Tibor KubÃk, François Guibault, Michal Å panÄl +1
Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. However, datasets suitable for training such…
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…
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…
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 KubÃk, François Guibault, Michal Å panÄl +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…