677 citations · 1.6k across the 25 of their papers we have counts for
26 papers
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…
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning
Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski +4
Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predi…
A novel adversarial learning strategy for medical image classification
Zong Fan, Xiaohui Zhang, Jacob A. Gasienica +7
Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and opt…
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Ling Huang, Thierry Denoeux, Pierre Vera +1
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep…
Application of belief functions to medical image segmentation: A review
Ling Huang, Su Ruan, Thierry Denoeux
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertaint…
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…
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Amine Amyar, Romain Modzelewski, Pierre Vera +2
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a…
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Ling Huang, Su Ruan, Pierre Decazes +1
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (…
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate brain tumor segmentation. The main problem is that not all types of MRIs are always available in clinica…
A Tri-attention Fusion Guided Multi-modal Segmentation Network
Tongxue Zhou, Su Ruan, Pierre Vera +1
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. Considering the correlation between…
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar +3
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based…
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Ling Huang, Thierry Denoeux, David Tonnelet +2
Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designin…
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Brain tumor is one of the most high-risk cancers which causes the 5-year survival rate of only about 36%. Accurate diagnosis of brain tumor is critical for the treatment planning.…
Evidential segmentation of 3D PET/CT images
Ling Huang, Su Ruan, Pierre Decazes +1
PET and CT are two modalities widely used in medical image analysis. Accurately detecting and segmenting lymphomas from these two imaging modalities are critical tasks for cancer s…
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and t…
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…
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-mo…
Belief function-based semi-supervised learning for brain tumor segmentation
Ling Huang, Su Ruan, Thierry Denoeux
Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However,…
Covid-19 classification with deep neural network and belief functions
Ling Huang, Su Ruan, Thierry Denoeux
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to d…
A review: Deep learning for medical image segmentation using multi-modality fusion
Tongxue Zhou, Su Ruan, Stéphane Canu
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusi…
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
Tongxue Zhou, Stéphane Canu, Su Ruan
The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19.…
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Amine Amyar, Su Ruan, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in i…
Weakly Supervised PET Tumor Detection Using Class Response
Amine Amyar, Romain Modzelewski, Pierre Vera +2
One of the most challenges in medical imaging is the lack of data and annotated data. It is proven that classical segmentation methods such as U-NET are useful but still limited du…
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Tongxue Zhou, Stéphane Canu, Pierre Vera +1
Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. Sinc…