deep-learning

TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes

The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end,we introduce TransforMesh, a spatio-temporal network based on transformers thatmodels …

Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform

Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional image information for classifying patients. However, little research …

Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data

Deep Neural Networks (DNNs) have an enormous potential to learn from complex biomedical data. In particular, DNNs have been used to seamlessly fuse heterogeneous information from neuroanatomy, genetics, biomarkers, and neuropsychological tests for …

Semi-Structured Deep Piecewise Exponential Models

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 models and thereby supports various survival tasks, such as competing …

Recalibration of Neural Networks for Point Cloud Analysis

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 extract local features, such as CNNs. While re-calibration has been …

Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers

Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up …

Adversarial Learned Molecular Graph Inference and Generation

Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, training requires solving an expensive graph isomorphism problem, which …

'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

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 shortcoming by learning a new class from a few annotated support …

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

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 tabular clinical data (demographics, biomarkers) are fused in a …