Search Results for author:
Found 5 papers, 1 papers with code
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing.
Single-Image based unsupervised joint segmentation and denoising
To this end, we combine the advantages of a variational segmentation method with the power of a self-supervised, single-image based deep learning approach.
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks.
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties.
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis.
