277 citations · 315 across the 9 of their papers we have counts for
10 papers
Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression
Aghiles Kebaili, Romain Modzelewski, Jérôme Lapuyade-Lahorgue +3
Glioma, an aggressive brain malignancy characterized by rapid progression and its poor prognosis, poses significant challenges for accurate evolution prediction. These challenges a…
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing M…
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurat…
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Despite the increasing use of deep learning in medical image segmentation, the limited availability of annotated training data remains a major challenge due to the time-consuming d…
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentati…
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan
Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where…
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction
Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep ne…
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy
Thibaud Brochet, Jerome Lapuyade-Lahorgue, Sebastien Bougleux +2
Pulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE im…
The epimorphisms of the category Haus are exactly the image-dense morphisms
Jérôme Lapuyade-Lahorgue
This document presents the proof that the epimorphisms of the category of Hausdorff spaces are exactly the image dense morphisms. While it is a classical result; its proof is diffi…
Groups of order 8 and 16
Jérôme Lapuyade-Lahorgue
This document is rather a course about groups than a research paper. However, it can be of interest for many master students in mathematics which are devoted to the p-group classif…