Search Results for author:
Found 36 papers, 7 papers with code
AutoPETIII: The Tracer Frontier. What Frontier?
For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans.
Deform-Mamba Network for MRI Super-Resolution
Thanks to the extracted features of the encoder, which include content-adaptive local and efficient global information, the vision Mamba decoder finally generates high-quality MR images.
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
Inspired by Mamba, our approach aims to learn the self-prior multi-scale contextual features under Mamba-UNet networks, which may help to super-resolve low-resolution medical images in an efficient way.
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes
Deep learning has gained significant attention in medical image segmentation.
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
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 data acquisition and privacy regulations.
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
We conduct experiments on two publicly available datasets, MICCAI's Brain Tumor Segmentation Challenge (BRATS), and Head and Neck Tumor Segmentation Challenge (HECKTOR), demonstrating the effectiveness of our method on different medical imaging modalities.
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis.
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review
Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning
In this paper, we present a deep learning-based brain tumor recurrence location prediction network.
Evidence fusion with contextual discounting for multi-modality medical image segmentation
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks.
A novel adversarial learning strategy for medical image classification
Deep learning (DL) techniques have been extensively utilized for medical image classification.
Application of belief functions to medical image segmentation: A review
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation.
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 on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications.
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
The architecture is composed of a deep feature-extraction module and an evidential layer.
Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing multi-scale feature maps of different levels based on the encoder-decoder architecture.
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities
Based on the fact that there is a strong correlation between MR modalities of the same patient, in this work, we propose a novel brain tumor segmentation network in the case of missing one or more modalities.
A Tri-attention Fusion Guided Multi-modal Segmentation Network
Considering the correlation between different MR modalities, in this paper, we propose a multi-modality segmentation network guided by a novel tri-attention fusion.
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns.
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of evidence using Dempster-Shafer theory (DST).
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities
The proposed network consists of a conditional generator, a correlation constraint network and a segmentation network.
Evidential segmentation of 3D PET/CT images
In this paper, a segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images.
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities
In addition, multi-modal MR images can provide complementary information for accurate brain tumor segmentation.
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy
Our method is tested on one POE dataset including 2947 distinct images, is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting.
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint
Our network includes N model-independent encoding paths with N image sources, a correlation constraint block, a feature fusion block, and a decoding path.
Belief function-based semi-supervised learning for brain tumor segmentation
Precise segmentation of a lesion area is important for optimizing its treatment.
Covid-19 classification with deep neural network and belief functions
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19.
A review: Deep learning for medical image segmentation using multi-modality fusion
Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation.
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health.
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions.
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain tumor segmentation.
Weakly Supervised PET Tumor Detection Using Class Response
In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-level.
Medical Image Synthesis with Deep Convolutional Adversarial Networks
To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire
Sparse representation learning has recently gained a great success in signal and image processing, thanks to recent advances in dictionary learning.
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
To better model the nonlinear relationship from MRI to CT and to produce more realistic images, we propose to use the adversarial training strategy and an image gradient difference loss function.
