Publications (8)
Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent
Mohit Kumar, Alexander Valentinitsch, Magdalena Fuchs +5
This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generali…
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke +1
Multi-class segmentation of vertebrae is a non-trivial task mainly due to the high correlation in the appearance of adjacent vertebrae. Hence, such a task calls for the considerati…
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch +2
Purpose: To use and test a labelling algorithm that operates on two-dimensional (2D) reformations, rather than three-dimensional (3D) data to locate and identify vertebrae. Methods…
Operator-Theoretic Framework for Gradient-Free Federated Learning
Mohit Kumar, Mathias Brucker, Alexander Valentinitsch +4
Federated learning must address heterogeneity, strict communication and computation limits, and privacy while ensuring performance. We propose an operator-theoretic framework that…
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch +3
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function c…
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Anjany Sekuboyina, Markus Rempfler, Jan KukaÄka +4
Robust localisation and identification of vertebrae is essential for automated spine analysis. The contribution of this work to the task is two-fold: (1) Inspired by the human expe…
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis +7
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…