Search Results for author: Tommy Löfstedt

Found 9 papers, 2 papers with code

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

1 code implementation19 Dec 2021 Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, 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 Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat AK, Sarahi Rosas-González, Illyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, 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-Andr Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, 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 metric developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.

Brain Tumor Segmentation Translation +1

A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation

no code implementations22 Apr 2021 Minh H. Vu, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt

Among different types of deep learning models, convolutional neural networks have been among the most successful and they have been used in many applications in medical imaging.

Incremental Learning Semantic Segmentation

Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation

no code implementations16 Nov 2020 Minh H. Vu, Tufve Nyholm, Tommy Löfstedt

Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice.

Denoising Tumor Segmentation

Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation

no code implementations19 Dec 2019 Minh H. Vu, Guus Grimbergen, Tufve Nyholm, Tommy Löfstedt

In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs.

Medical Image Segmentation Semantic Segmentation

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

no code implementations11 Oct 2019 Minh H. Vu, Tufve Nyholm, Tommy Löfstedt

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord.

Brain Tumor Segmentation Tumor Segmentation

A general multiblock method for structured variable selection

no code implementations29 Oct 2016 Tommy Löfstedt, Fouad Hadj-Selem, Vincent Guillemot, Cathy Philippe, Nicolas Raymond, Edouard Duchesney, Vincent Frouin, Arthur Tenenhaus

However, for technical reasons, the variable selection offered by SGCCA was restricted to a covariance link between the blocks (i. e., with $\tau=1$).

Variable Selection

Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty

no code implementations6 Sep 2016 Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay

However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.

Cannot find the paper you are looking for? You can Submit a new open access paper.