research
∙
09/12/2023
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Single-modality medical images generally do not contain enough informati...
share
research
∙
07/24/2023
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
Deep learning has become a popular tool for medical image analysis, but ...
research
∙
06/23/2022
Evidence fusion with contextual discounting for multi-modality medical image segmentation
As information sources are usually imperfect, it is necessary to take in...
research
∙
06/23/2022
A novel adversarial learning strategy for medical image classification
Deep learning (DL) techniques have been extensively utilized for medical...
research
∙
05/03/2022
Application of belief functions to medical image segmentation: A review
Belief function theory, a formal framework for uncertainty analysis and ...
research
∙
03/22/2022
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction
In this paper, we propose to quantitatively compare loss functions based...
research
∙
03/01/2022
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Background and Objectives: Predicting patient response to treatment and ...
research
∙
01/31/2022
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
An automatic evidential segmentation method based on Dempster-Shafer the...
research
∙
12/20/2021
Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
Due to the irregular shapes,various sizes and indistinguishable boundari...
research
∙
11/08/2021
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities
Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accur...
research
∙
11/02/2021
A Tri-attention Fusion Guided Multi-modal Segmentation Network
In the field of multimodal segmentation, the correlation between differe...
research
∙
10/20/2021
Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics
Artificial intelligence (AI) techniques have significant potential to en...
research
∙
08/11/2021
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Lymphoma detection and segmentation from whole-body Positron Emission To...
research
∙
04/27/2021
Evidential segmentation of 3D PET/CT images
PET and CT are two modalities widely used in medical image analysis. Acc...
research
∙
04/13/2021
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to a...
research
∙
04/12/2021
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy
Pulmonary optical endomicroscopy (POE) is an imaging technology in real ...
research
∙
02/05/2021
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
In the field of multimodal segmentation, the correlation between differe...
research
∙
01/29/2021
Belief function-based semi-supervised learning for brain tumor segmentation
Precise segmentation of a lesion area is important for optimizing its tr...
research
∙
01/18/2021
Covid-19 classification with deep neural network and belief functions
Computed tomography (CT) image provides useful information for radiologi...
research
∙
04/22/2020
A review: Deep learning for medical image segmentation using multi-modality fusion
Multi-modality is widely used in medical imaging, because it can provide...
research
∙
04/14/2020
An automatic COVID-19 CT segmentation based on U-Net with attention mechanism
The coronavirus disease (COVID-19) pandemic has led a devastating effect...
research
∙
04/14/2020
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating eff...
research
∙
03/19/2020
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate ...
research
∙
03/19/2020
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
One of the most challenges in medical imaging is the lack of data. It is...
research
∙
03/18/2020
Weakly Supervised PET Tumor Detection Using Class Response
One of the most challenges in medical imaging is the lack of data and an...
research
∙
03/18/2020
Weakly Supervised PET Tumor Detection UsingClass Response
One of the most challenges in medical imaging is the lack of data and an...
research
∙
09/12/2017
Une véritable approche ℓ_0 pour l'apprentissage de dictionnaire
Sparse representation learning has recently gained a great success in si...
research
∙
12/16/2016
