Search Results for author: Eugene Vorontsov

Found 16 papers, 8 papers with code

Image-level supervision and self-training for transformer-based cross-modality tumor segmentation

no code implementations17 Sep 2023 Malo de Boisredon, Eugene Vorontsov, William Trung Le, Samuel Kadoury

An image-to-image translation strategy between imaging modalities is used to produce annotated pseudo-target volumes and improve generalization to the unannotated target modality.

Brain Tumor Segmentation Image Segmentation +4

M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision

1 code implementation14 Dec 2022 Malo Alefsen de Boisredon d'Assier, Eugene Vorontsov, Samuel Kadoury

Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality.

Brain Tumor Segmentation Domain Adaptation +3

Label noise in segmentation networks : mitigation must deal with bias

no code implementations5 Jul 2021 Eugene Vorontsov, Samuel Kadoury

In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data.

Image Segmentation Medical Image Segmentation +2

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

1 code implementation NeurIPS 2019 Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary.

Towards annotation-efficient segmentation via image-to-image translation

no code implementations2 Apr 2019 Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham, Jan Kautz, Samuel Kadoury

Specifically, we propose a semi-supervised framework that employs unpaired image-to-image translation between two domains, presence vs. absence of cancer, as the unsupervised objective.

Brain Tumor Segmentation Image-to-Image Translation +3

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

On orthogonality and learning RNNs with long term dependencies

no code implementations ICML 2017 Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.

Liver lesion segmentation informed by joint liver segmentation

no code implementations24 Jul 2017 Eugene Vorontsov, An Tang, Chris Pal, Samuel Kadoury

We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.

Computed Tomography (CT) Lesion Detection +3

Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

no code implementations16 Feb 2017 Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov, Lisa Di Jorio, An Tang, Adriana Romero, Yoshua Bengio, Chris Pal, Samuel Kadoury

Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.

Image Segmentation Medical Image Segmentation +2

On orthogonality and learning recurrent networks with long term dependencies

1 code implementation31 Jan 2017 Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.

The Importance of Skip Connections in Biomedical Image Segmentation

1 code implementation14 Aug 2016 Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, Chris Pal

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation.

Image Segmentation Semantic Segmentation

Metastatic liver tumour segmentation from discriminant Grassmannian manifolds

no code implementations31 Aug 2015 Samuel Kadoury, Eugene Vorontsov, An Tang

The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours.


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