deep learning

While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be …

EasyReg is an extension that builds on Mermaid, providing a simple interface to Mermaid and other popluar registration packages. The currently supported methods include Mermaid-optimization (i.e., optimization-based registration) and Mermaid-network (i.

Deep segmentation networks typically create their outputs via a softmax. Hence, the assignment appears to be probabilistic. However, it has been shown in literatute that interpreting these softmax outputs as label probabilities is not reliable and that they tend to be overly confident.

VoteNet is a deep-learning-based label fusion strategy for multi-atlas segmentation (MAS) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. By selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct deep-learning (DL) segmentation approach.

Analysis approaches for histology images; in particular, for appearance normalization.

Deformable image registration approaches via numerical optimization and deep learning.

We have developed multiple image segmentation methods.

Image analysis approaches for the quantitative analysis of osteoarthritis in the knee.

Topological analysis approaches, with a recent focus on integrating deep learning with topology.

Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE). Although important steps have been taken to realize the advantages of such …