deep-learning

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 …

Likelihood-Free Inference and Generation of Molecular Graphs

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 …