Fuzzy versus hard hidden Markov chains segmentation for volume determination and quantitation in noisy PET images. - Mathieu Hatt; Nicolas Boussion; Frédéric Lamare; Christophe Collet 001; Fabien Salzenstein; Christian Roux; Yves Bizais; Catherine Cheze Le Rest; Dimitris Visvikis (ISBI, 2006)

3d Fuzzy Adaptive Unsupervised Bayesian Segmentation for Volume Determination in Pet. - Mathieu Hatt; Christian Roux; Dimitris Visvikis (ISBI, 2007)

Non-stationary fuzzy Markov chain. - Fabien Salzenstein; Christophe Collet 001; Steven Le Cam; Mathieu Hatt (Pattern Recognit. Lett., 2007)

Conditional partial volume correction for emission tomography: A wavelet-based hidden Markov model and multi-resolution approach. - Adrien Le Pogam; Mathieu Hatt; Nicolas Boussion; Denis Guilloteau; Jean-Louis Baulieu; Caroline Prunier; Federico E. Turkheimer; Dimitris Visvikis (ISBI, 2008)

Contrast enhancement in emission tomography by way of synergistic PET/CT image combination. - Nicolas Boussion; Mathieu Hatt; Frédéric Lamare; Catherine Cheze Le Rest; Dimitris Visvikis (Comput. Methods Programs Biomed., 2008)

Détermination automatique des volumes fonctionnels en imagerie d'émission pour les applications en oncologie. (Automatic delineation of functional volumes in emission tomography for oncology applications). - Mathieu Hatt (2008)

Incorporating Patient-Specific Variability in the Simulation of Realistic Whole-Body 18hboxF-FDG Distributions for Oncology Applications. - Amandine Le Maitre; William Paul Segars; Simon Marache; Anthonin Reilhac; Mathieu Hatt; Sandrine Tomeï; Carole Lartizien; Dimitris Visvikis (Proc. IEEE, 2009)

A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET. - Mathieu Hatt; Catherine Cheze Le Rest; Alexandre Turzo; Christian Roux; Dimitris Visvikis (IEEE Trans. Medical Imaging, 2009)

Multi modal images analysis and processing in oncology. (Analyse et traitement d'images multi modales en oncologie). - Mathieu Hatt (2012)

Image Change Detection Using Paradoxical Theory for Patient Follow-Up Quantitation and Therapy Assessment. - Simon David; Dimitris Visvikis; Gwenolé Quellec; Catherine Cheze Le Rest; Philippe Fernandez; Michèle Allard; Christian Roux; Mathieu Hatt (IEEE Trans. Medical Imaging, 2012)

Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation. - Adrien Le Pogam; H. Hanzouli; Mathieu Hatt; Catherine Cheze Le Rest; Dimitris Visvikis (Medical Image Anal., 2013)

Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis. - Taman Upadhaya; Yannick Morvan; Eric Stindel; Pierre-Jean Le Reste; Mathieu Hatt (ISBI, 2015)

Regarding "Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm" By DP. Onoma et al. - Mathieu Hatt; Dimitris Visvikis (Comput. Medical Imaging Graph., 2015)

Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices. - Taman Upadhaya; Yannick Morvan; Eric Stindel; Pierre-Jean Le Reste; Mathieu Hatt (Computer-Aided Diagnosis, 2016)

Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort. - Marie-Charlotte Desseroit; Florent Tixier; Wolfgang Weber; Barry A. Siegel; Catherine Cheze Le Rest; Dimitris Visvikis; Mathieu Hatt (CoRR, 2016)

Image Filtering with Advectors. - Vincent Jaouen; Laurent Gaubert; Julien Bert; Mathieu Hatt; Dimitris Visvikis (ICIP, 2018)

The first MICCAI challenge on PET tumor segmentation. - Mathieu Hatt; Baptiste Laurent; Anouar Ouahabi; Hadi Fayad; Shan Tan; Laquan Li; Wei Lu 025; Vincent Jaouen; Clovis Tauber; Jakub Czakon; Filip Drapejkowski; Witold Dyrka; et al. (Medical Image Anal., 2018)

Encoder-Decoder Network for Brain Tumor Segmentation on Multi-sequence MRI. - Andrei Iantsen; Vincent Jaouen; Dimitris Visvikis; Mathieu Hatt (BrainLes@MICCAI (2), 2019)

PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients. - Isaac Shiri; Hassan Maleki; Ghasem Hajianfar; Hamid Abdollahi; Saeed Ashrafinia; Mathieu Hatt; Mehrdad Oveisi; Arman Rahmim (CoRR, 2019)

Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches. - Isaac Shiri; Hassan Maleki; Ghasem Hajianfar; Hamid Abdollahi; Saeed Ashrafinia; Mathieu Hatt; Mehrdad Oveisi; Arman Rahmim (CoRR, 2019)

Image Enhancement With PDEs and Nonconservative Advection Flow Fields. - Vincent Jaouen; Julien Bert; Nicolas Boussion; Hadi Fayad; Mathieu Hatt; Dimitris Visvikis (IEEE Trans. Image Process., 2019)

Squeeze-and-Excitation Normalization for Brain Tumor Segmentation. - Andrei Iantsen; Vincent Jaouen; Dimitris Visvikis; Mathieu Hatt (BrainLes@MICCAI (2), 2020)

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images. - Andrei Iantsen; Dimitris Visvikis; Mathieu Hatt (HECKTOR@MICCAI, 2020)

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images. - Vincent Andrearczyk; Valentin Oreiller; Sarah Boughdad; Catherine Cheze Le Rest; Hesham Elhalawani; Mario Jreige; John O. Prior; Martin Vallières; Dimitris Visvikis; Mathieu Hatt; Adrien Depeursinge (HECKTOR@MICCAI, 2021)

Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography. - Lennart Brocki; Wistan Marchadour; Jonas Maison; Bogdan Badic; Panagiotis G. Papadimitroulas; Mathieu Hatt; Franck Vermet; Neo Christopher Chung (EXTRAAMAS@AAMAS, 2022)

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. - Vincent Andrearczyk; Valentin Oreiller; Moamen Abobakr; Azadeh Akhavanallaf; Panagiotis Balermpas; Sarah Boughdad; Leo Capriotti; Joël Castelli; Catherine Cheze Le Rest; Pierre Decazes; Ricardo Correia 006; Dina El-Habashy; et al. (HECKTOR@MICCAI, 2022)

Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images. - Hui Xu; Yihao Li; Wei Zhao; Gwenolé Quellec; Lijun Lu; Mathieu Hatt (HECKTOR@MICCAI, 2022)

Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. - Valentin Oreiller; Vincent Andrearczyk; Mario Jreige; Sarah Boughdad; Hesham Elhalawani; Joël Castelli; Martin Vallières; Simeng Zhu; Juanying Xie; Ying Peng; Andrei Iantsen; Mathieu Hatt; et al. (Medical Image Anal., 2022)

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision. - Jianning Li 002; Antonio Pepe 003; Christina Gsaxner; Gijs Luijten; Yuan Jin; Narmada Ambigapathy; Enrico Nasca; Naida Solak; Gian Marco Melito; Afaque R. Memon; Xiaojun Chen 003; Jan Stefan Kirschke; et al. (CoRR, 2023)

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge. - Vincent Andrearczyk; Valentin Oreiller; Sarah Boughdad; Catherine Cheze Le Rest; Olena Tankyevych; Hesham Elhalawani; Mario Jreige; John O. Prior; Martin Vallières; Dimitris Visvikis; Mathieu Hatt; Adrien Depeursinge (Medical Image Anal., 2023)

A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms. - Aya Hage Chehade; Nassib Abdallah; Jean-Marie Marion; Mathieu Hatt; Mohamad Oueidat; Pierre Chauvet (SN Comput. Sci., 2024)

Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification. - Aya Hage Chehade; Nassib Abdallah; Jean-Marie Marion; Mathieu Hatt; Mohamad Oueidat; Pierre Chauvet (Artif. Intell. Medicine, 2025)

Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14. - Aya Hage Chehade; Nassib Abdallah; Jean-Marie Marion; Mathieu Hatt; Mohamad Oueidat; Pierre Chauvet (Comput. Methods Programs Biomed., 2025)