7 papers
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…
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…
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction
Benjamin Gutierrez-Becker, Sergios Gatidis, Daniel Gutmann +3
Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological…
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
Benjamin Gutierrez-Becker, Christian Wachinger
We propose a deep neural network for supervised learning on neuroanatomical shapes. The network directly operates on raw point clouds without the need for mesh processing or the id…
Detect, Quantify, and Incorporate Dataset Bias: A Neuroimaging Analysis on 12,207 Individuals
Christian Wachinger, Benjamin Gutierrez Becker, Anna Rieckmann
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 mode…