Zonal-Aware Prostate MRI Modeling With Uncertainty Quantification for Reliable Detection of Clinically Significant Prostate Cancer
C. Phung-Ngoc, A. Bousse, A. De Paepe, T. Merlin, B. Laurent, H-P. Dang, O. Saut, C. Cheze-Le-Rest, D. Visvikis. Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space.
IEEE Transactions on Radiation and Plasma Medical Sciences, 2026. [pdf]
C. Phung-Ngoc, A. De Paepe, H-P. Dang, O. Saut, D. Visvikis, A. Bousse. Joint Reconstruction of Activity and Attenuation with Motion Estimation in PET using Diffusion Models.
2026 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2026.
T-L. Tran, C. Elvira, H-P. Dang, C. Herzet. One to beat them all:"RYU"-a unifying framework for the construction of safe balls.
Open Journal of Mathematical Optimization, 2025, 6, pp.1-16. [pdf]
V. H. Pham, N. Nguyen, M-Q. Cao, H-P. Dang, T-L Tran. Stability Radius and an Upgrading Model of Median Location on Trees.
Asia-Pacific Journal of Operational Research, 2025. [pdf]
K. Taguelmimt, G. A. Miranda, H. Harb, T. T. Thanh, D. Visvikis, H-P. Dang, B. Malavaud, J. Bert. Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning.
Computers in Biology and Medicine, Jun. , 2025, 110440. [pdf]
C. Phung-Ngoc, A. Bousse, A. De Paepe, H-P. Dang, O. Saut, D. Visvikis. 3D Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling.
2025 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), Nov. , 2025
C. Phung-Ngoc, A. Bousse, A. De Paepe, H-P. Dang, O. Saut, D. Visvikis. Joint Reconstruction of the Activity and the Attenuation in PET by Diffusion Posterior Sampling: a Feasibility Study.
Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), May, 2025. [pdf]
K. Taguelmimt, H-P. Dang, G. A. Miranda, D. Visvikis, B. Malavaud, J. Bert. PI-RADS inspired architecture: A new deep learning approach to detect clinically significant prostate cancer on MRI.
Proc. of the 2025 IEEE International Symposium on Biomedical Imaging (ISBI), Apr. 14, 2025.
G. Andrade-Miranda, P. S. Vega, K. Taguelmimt, H-P. Dang, D. Visvikis, J. Bert. Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.
Computerized Medical Imaging and Graphics, November 2024. [pdf]
K. Taguelmimt, H-P. Dang, G. A. Miranda, D. Visvikis, B. Malavaud, J. Bert. Uncertainty-Aware Deep Learning Classification for MRI-based Prostate Cancer Detection.
In CaPTion 2024: MICCAI Workshop on Cancer Prevention through Early Detection, Morocco, October 2024.
H-P. Dang, T. Wojdacki, D. Visvikis, J. Bert. Apprentissage profond pour améliorer la qualité statistique et le temps de calcul de carte dosimétrique en radiothérapie.
Conférence Extraction et Gestion des Connaissances (EGC) - IA pour la Santé, Dijon, 22-26 Janvier 2024.
J. Arbel, H-P. Dang, C. Elvira, C. Herzet, Z. Naulet, M. Vladimirova. Bayes in action in deep learning and dictionary learning.
ESAIM: Proceedings and Surveys, vol. 74, 90-107, 2023.[pdf]
T-L. Tran, C. Elvira, H-P. Dang, C. Herzet. Dimensionality reduction for convex optimization based on safe regions: A unified approach.
In Vietnam Mathematical Congress, 2023.
A. Touil, H-P. Dang, D. Benoit, D. Visvikis, J. Bert. Toward a generic approach for dose distribution recovery by using Deep Learning and GPU-based Geant4 simulations.
Proc. of the IV Geant4 International User Conference at the physics-medicine-biology frontier, 2022.[pdf]
T-L. Tran, C. Elvira, H-P. Dang, C. Herzet. Beyond GAP screening for Lasso by exploiting new dual cutting half-spaces.
Proc. of the 30th European Signal Processing Conference (EUSIPCO), 2022.[pdf]
C. Herzet ,C. Elvira, H-P. Dang. Region-free safe screening test for l1 penalized convex problems.
Proc. of the 30th European Signal Processing Conference (EUSIPCO), 2022.[pdf]
T-L. Tran, C. Elvira, H-P. Dang, C. Herzet. Une nouvelle méthode d’accélération pour Lasso par élimination sûre de variables.
Conférence sur l’Apprentissage automatique (CAp), 2022.[pdf]
H-P. Dang, J. Bert, D. Visvikis. Improving statistical quality and computational time with adapting deep learning for Monte Carlo dose distribution.
Recherche en Imagerie et Technologies pour la Santé, 2022.
C. C. Aguida, H-P. Dang, A. Monnereau, B. Vacquier, S. Orazio. Modélisation statistique du lien entre l’exposition indirecte aux produits phytosanitaires agricoles et le risque de survenue d’une hémopathie maligne (HM) en France : proposition d’une méthode spatialisée.
Journées de méthodologie statistique de l’Insee, 2022.[pdf]
H-P. Dang, M. Vimond, S. Geffray. Data-Driven Parameter Choice for Illumination Artifact Correction of Digital Images.
IEEE Signal Processing Letters (SPL), vol. 28 : 155-159, 2021.[pdf]
H-P. Dang. Interaction entre les méthodes bayésiennes et optimisation en utilisant Small-Variance Asymptotics dans le cadre de l’apprentissage de dictionnaire.
Journées Modélisation Aléatoire et Statistique (MAS), 2020.
H-P. Dang, C. Elvira. Parameter-free Small Variance Asymptotics for Dictionary Learning.
Proc. of the 27th European Signal Processing Conference (EUSIPCO), 2019.[pdf]
H-P. Dang, M.Vimond. Segmentation adaptative d’image avec un nombre efficace de classes en utilisant l’algorithme Expectation-Maximisation pour modèle de mélange par processus de Dirichlet tronqué.
Conférence sur l’Apprentissage automatique (CAp), 2019.
H-P. Dang, P. Chainais. Towards dictionaries of optimal size : a bayesian non parametric approach.
Journal of Signal Processing Systems (JSPS), vol. 90, issue 2 : 221–232, 2018.[pdf]
C. Elvira, H-P. Dang, P. Chainais. Small variance asymptotics and bayesian nonparametrics for dictionary learning.
Proc. of the 26th European Signal Processing Conference (EUSIPCO), 2018.[pdf]
H-P. Dang, C. Elvira, P. Chainais. Vers une méthode d’optimisation non paramétrique pour l’apprentissage de dictionnaire en utilisant Small-Variance Asymptotics pour modèle probabiliste.
Conférence sur l’Apprentissage automatique (CAp), 2018.
H-P. Dang, P. Chainais. Apprentissage de dictionnaire non paramétrique pour les problèmes inverses en traitement d’image.
Journées de Statistique, 2018.
H-P. Dang, P. Chainais. Indian buffet process dictionary learning : algorithms and applications to image processing.
International Journal of Approximate Reasoning (IJAR), 83 : 1-20, 2017.[pdf]
H-P. Dang. Approches bayésiennes non paramétriques et apprentissage de dictionnaire pour les problèmes inverses en traitement d’image.
Thesis, Centrale Lille, 2016.[pdf]
H-P. Dang, P. Chainais. Indian buffet process dictionary learning for image inpainting.
IEEE Workshop on Statistical Signal Processing (SSP), 2016.[pdf]
E. Rault, T. Lacornerie, H-P. Dang, F. Crop, E. Lartigau, N. Reynaert, D. Pasquier. Accelerated partial breast irradiation using robotic radiotherapy : a dosimetric comparison with tomotherapy and three-dimensional conformal radiotherapy.
Radiation Oncology, 11(1), 2, 2016.
H-P. Dang, P. Chainais. A bayesian non parametric approach to learn dictionaries with adapted numbers of atoms.
IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 1–6, Intel best paper award, 2015.[pdf]
H-P. Dang, P. Chainais. Approche bayésienne non paramétrique dans l’apprentissage du dictionnaire pour adapter le nombre d’atomes.
Conférence nationale Gretsi, 2015.
E. Rault, T. Lacornerie, H-P. Dang, E. Lartigau, N. Reynaert, D. Pasquier. EP-1610 : Accelerated partial breast irradiation using the CyberKnife : A feasibility study.
Radiotherapy and Oncology, no 111, S207-S208, 2014.