367 citations · 696 across the 18 of their papers we have counts for
7 papers · 1 filter
Recalibration of Neural Networks for Point Cloud Analysis
Ignacio Sarasua, Sebastian Poelsterl, Christian Wachinger
Spatial and channel re-calibration have become powerful concepts in computer vision. Their ability to capture long-range dependencies is especially useful for those networks that e…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks
Benjamin Gutierrez Becker, Ignacio Sarasua, Christian Wachinger
We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered…
Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers
J. Senapati, A. Guha Roy, S. Pölsterl +6
Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially acros…
Importance Driven Continual Learning for Segmentation Across Domains
Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy +1
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend…
Recalibrating 3D ConvNets with Project & Excite
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +1
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks term…