367 citations · 696 across the 18 of their papers we have counts for
7 papers · 1 filter
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Sebastian Pölsterl, Ignacio Sarasua, Benjamín Gutiérrez-Becker +1
We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and…
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann +1
Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex mach…
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +2
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and varia…
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult fo…
Adversarial Learned Molecular Graph Inference and Generation
Sebastian Pölsterl, Christian Wachinger
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, trainin…
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this…