386 citations · 686 across the 16 of their papers we have counts for
16 papers
Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms
Marius Dubosc, Yann Fischer, Zacharie Auray +4
Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of reti…
Strong relations between discrete surfaces, poset-based connected manifolds, and normal pseudomanifolds
Nicolas Boutry
In this paper, we study some relationships existing between some particular mathematical structures: discrete surfaces coming from discrete topology and mathematical morphology, po…
Found in Translation: semantic approaches for enhancing AI interpretability in face verification
Miriam Doh, Caroline Mazini Rodrigues, N. Boutry +3
The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to…
Explaning with trees: interpreting CNNs using hierarchies
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
Challenges persist in providing interpretable explanations for neural network reasoning in explainable AI (xAI). Existing methods like Integrated Gradients produce noisy maps, and…
Bridging Human Concepts and Computer Vision for Explainable Face Verification
Miriam Doh, Caroline Mazini Rodrigues, Nicolas Boutry +3
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairnes…
Transforming gradient-based techniques into interpretable methods
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
The explication of Convolutional Neural Networks (CNN) through xAI techniques often poses challenges in interpretation. The inherent complexity of input features, notably pixels ex…
Unsupervised discovery of Interpretable Visual Concepts
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such…
Discrete Morse Functions and Watersheds
Gilles Bertrand, Nicolas Boutry, Laurent Najman
Any watershed, when defined on a stack on a normal pseudomanifold of dimension d, is a pure (d -- 1)-subcomplex that satisfies a drop-of-water principle. In this paper, we introduc…
BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation
Minh On Vu Ngoc, Yizi Chen, Nicolas Boutry +2
Capturing the global topology of an image is essential for proposing an accurate segmentation of its domain. However, most of existing segmentation methods do not preserve the init…
A Proof of the Tree of Shapes in n-D
Thierry GÉraud, Nicolas Boutry, Sébastien Crozet +2
In this paper, we prove that the self-dual morphological hierarchical structure computed on a n-D gray-level wellcomposed image u by the algorithm of G{é}raud et al. [1] is exactly…
Some equivalence relation between persistent homology and morphological dynamics
Nicolas Boutry, Laurent Najman, Thierry Géraud
In Mathematical Morphology (MM), connected filters based on dynamics are used to filter the extrema of an image. Similarly, persistence is a concept coming from Persistent Homology…
Gradient Vector Fields of Discrete Morse Functions and Watershed-cuts
Nicolas Boutry, Gilles Bertrand, Laurent Najman
In this paper, we study a class of discrete Morse functions, coming from Discrete Morse Theory, that are equivalent to a class of simplicial stacks, coming from Mathematical Morpho…
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
Tianyi Shi, Nicolas Boutry, Yongchao Xu +1
Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmen…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Sharib Ali, Mariia Dmitrieva, Noha Ghatwary +34
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…