Search Results for author: Maximilian Baust

Found 15 papers, 2 papers with code

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

2 code implementations6 Jun 2023 Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar

Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.

Operator learning

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.

Federated Learning

Redefining Ultrasound Compounding: Computational Sonography

no code implementations5 Nov 2018 Rüdiger Göbl, Diana Mateus, Christoph Hennersperger, Maximilian Baust, Nassir Navab

By providing a novel paradigm for the acquisition and reconstruction of tracked freehand 3D ultrasound, this work presents the concept of Computational Sonography (CS) to model the directionality of ultrasound information.

CFCM: Segmentation via Coarse to Fine Context Memory

1 code implementation4 Jun 2018 Fausto Milletari, Nicola Rieke, Maximilian Baust, Marco Esposito, Nassir Navab

Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.

Decoder Image Segmentation +2

SobolevFusion: 3D Reconstruction of Scenes Undergoing Free Non-Rigid Motion

no code implementations CVPR 2018 Miroslava Slavcheva, Maximilian Baust, Slobodan Ilic

We present a system that builds 3D models of non-rigidly moving surfaces from scratch in real time using a single RGB-D stream.

3D Reconstruction

Understanding Regularization to Visualize Convolutional Neural Networks

no code implementations20 Apr 2018 Maximilian Baust, Florian Ludwig, Christian Rupprecht, Matthias Kohl, Stefan Braunewell

Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.

KillingFusion: Non-Rigid 3D Reconstruction Without Correspondences

no code implementations CVPR 2017 Miroslava Slavcheva, Maximilian Baust, Daniel Cremers, Slobodan Ilic

We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors.

3D Reconstruction Unity

Deep Active Contours

no code implementations18 Jul 2016 Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust, Nassir Navab

We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.

Interactive Segmentation

Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests

no code implementations23 Oct 2015 Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli, Lichao Wang, Andrea Remuzzi, Maximilian Baust, Nassir Navab

This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.

Computed Tomography (CT) Segmentation

Total Variation Regularization of Shape Signals

no code implementations CVPR 2015 Maximilian Baust, Laurent Demaret, Martin Storath, Nassir Navab, Andreas Weinmann

This paper introduces the concept of shape signals, i. e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals.

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