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Design Choices in Splitting-Based Self-Supervised Sparse-View CT Reconstruction
Nadja Gruber, Lukas Neumann, Ander Biguri +3
Self-supervised data splitting has emerged as a promising paradigm for sparse-view CT reconstruction, enabling training from incomplete measurements without fully sampled ground tr…
Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse
Markus Haltmeier, Nadja Gruber, Gyeongha Hwang
Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution. A central task is image reco…
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Markus Haltmeier, Lukas Neumann, Nadja Gruber +1
Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-truth images that are often unav…
HyDRA: Hybrid Denoising Regularization for Measurement-Only DEQ Training
Markus Haltmeier, Lukas Neumann, Nadja Gruber +2
Solving image reconstruction problems of the form \(\mathbf{A} \mathbf{x} = \mathbf{y}\) remains challenging due to ill-posedness and the lack of large-scale supervised datasets. D…
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise
Nadja Gruber, Johannes Schwab, Markus Haltmeier +3
We propose Noisier2Inverse, a correction-free self-supervised deep learning approach for general inverse problems. The proposed method learns a reconstruction function without the…
Self2Seg: Single-Image Self-Supervised Joint Segmentation and Denoising
Nadja Gruber, Johannes Schwab, Noémie Debroux +2
We develop Self2Seg, a self-supervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of variational segmentation with se…
Variational multichannel multiclass segmentation using unsupervised lifting with CNNs
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a rec…
Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging
Nadja Gruber, Johannes Schwab, Sebastien Court +2
We propose, analyze and realize a variational multiclass segmentation scheme that partitions a given image into multiple regions exhibiting specific properties. Our method determin…
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Nadja Gruber, Stephan Antholzer, Werner Jaschke +2
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. The segmentation of…