Search Results for author:
Found 12 papers, 1 papers with code
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.
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 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.
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.
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.
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.
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.
Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain tumor segmentation.
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.
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.
