Search Results for author: Samuel Kadoury

Found 25 papers, 4 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

End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction

no code implementations13 Mar 2023 Matej Gazda, Peter Drotar, Liset Vazquez Romaguera, Samuel Kadoury

Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating cancer patients.

Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction

no code implementations7 Mar 2023 William Trung Le, Chulmin Bang, Philippine Cordelle, Daniel Markel, Phuc Felix Nguyen-Tan, Houda Bahig, Samuel Kadoury

Accuracies of 85. 8% and 75. 3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment.

3D Classification

Prediction of a T-cell/MHC-I-based immune profile for colorectal liver metastases from CT images using ensemble learning

no code implementations6 Mar 2023 Ralph Saber, David Henault, Rolando Rebolledo, Simon Turcotte, Samuel Kadoury

To this end, we propose an ensemble network combining multiple Attentive Interpretable Tabular learning (TabNet) models, trained using CT-derived radiomic features.

Ensemble Learning

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

3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications

no code implementations3 Aug 2020 Hongliang Li, Tal Mezheritsky, Liset Vazquez Romaguera, Samuel Kadoury

Moreover, it is found that the speckle reduction using our deep learning model contributes to improving the 3D registration performance.

Image Segmentation Semantic Segmentation

A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction

no code implementations28 May 2020 William Le, Francisco Perdigón Romero, Samuel Kadoury

In medical imaging, radiological scans of different modalities serve to enhance different sets of features for clinical diagnosis and treatment planning.

Survival Prediction

A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction

no code implementations MIDL 2019 William Le, Francisco Perdigón Romero, Samuel Kadoury

Medical image classification performance worsens in multi-domain datasets, caused by radiological image differences across institutions, scanner manufacturer, model and operator.

Image Classification Medical Image Classification +1

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

End-to-End Discriminative Deep Network for Liver Lesion Classification

no code implementations28 Jan 2019 Francisco Perdigon Romero, Andre Diler, Gabriel Bisson-Gregoire, Simon Turcotte, Real Lapointe, Franck Vandenbroucke-Menu, An Tang, Samuel Kadoury

In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver.

Classification General Classification +1

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

Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US

no code implementations6 Jun 2018 Marc-Antoine Boucher, Sarah Lippe, Amelie Damphousse, Ramy El-Jalbout, Samuel Kadoury

The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly.


Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis

no code implementations6 Jun 2018 William Mandel, Olivier Turcot, Dejan Knez, Stefan Parent, Samuel Kadoury

A discriminant manifold is first constructed to maximize the separation between responsive and non-responsive groups of patients treated with AVBGM for scoliosis.

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

Prior-based Coregistration and Cosegmentation

no code implementations22 Jul 2016 Mahsa Shakeri, Enzo Ferrante, Stavros Tsogkas, Sarah Lippe, Samuel Kadoury, Iasonas Kokkinos, Nikos Paragios

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation.

Sub-cortical brain structure segmentation using F-CNN's

no code implementations5 Feb 2016 Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante, Sarah Lippe, Samuel Kadoury, Nikos Paragios, Iasonas Kokkinos

In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data.

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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