Search Results for author: Nicolas Boutry

Found 7 papers, 4 papers with code

Unsupervised discovery of Interpretable Visual Concepts

no code implementations31 Aug 2023 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.

BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation

1 code implementation23 Jul 2022 Minh On Vu Ngoc, Yizi Chen, Nicolas Boutry, Jonathan Fabrizio, Clement Mallet

Our method is an extension of the BALoss [1], in which we want to improve the leakage detection for better recovering the closeness property of the image segmentation.

Image Segmentation Semantic Segmentation

Some equivalence relation between persistent homology and morphological dynamics

no code implementations25 May 2022 Nicolas Boutry, Laurent Najman, Thierry Géraud

In Mathematical Morphology (MM), connected filters based on dynamics are used to filter the extrema of an image.

Topological Data Analysis

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

1 code implementation25 Feb 2022 Tianyi Shi, Nicolas Boutry, Yongchao Xu, Thierry Géraud

This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.

Crack Segmentation

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

1 code implementation19 Dec 2021 Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Datwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gomez, Pablo Arbelaez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-min Pei, Murat AK, Sarahi Rosas-Gonzalez, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Lofstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andre Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.

Benchmarking Brain Tumor Segmentation +3

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