Search Results for author: Minh H. Vu

Found 8 papers, 3 papers with code

LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space

1 code implementation21 Jul 2023 Lorenzo Tronchin, Minh H. Vu, Paolo Soda, Tommy Löfstedt

Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation.

Data Augmentation

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 +5

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.

Image Segmentation Incremental Learning +1

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.

Decoder Denoising +2

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.

Image Segmentation Medical Image Segmentation +2

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 Segmentation +1

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