While many studies have assessed the fairness of AI algorithms in the me...
Medical imaging models have been shown to encode information about patie...
In fetal ultrasound screening, Doppler images on the umbilical artery (U...
Segmentation uncertainty models predict a distribution over plausible
se...
Confounding information in the form of text or markings embedded in medi...
Out of distribution (OOD) medical images are frequently encountered, e.g...
Recent work on algorithmic fairness has largely focused on the fairness ...
Concept bottleneck models (CBMs) include a bottleneck of human-interpret...
Curvilinear structure segmentation plays an important role in many
appli...
Convolutional neural networks have enabled significant improvements in
m...
Standard spatial convolutions assume input data with a regular neighborh...
We present a graph neural network model for solving graph-to-graph learn...
Motivated by a challenging tubular network segmentation task, this paper...
We propose a semantic similarity metric for image registration. Existing...
Probabilistic image segmentation encodes varying prediction confidence a...
We propose a semantic similarity metric for image registration. Existing...
In optimization, the natural gradient method is well-known for likelihoo...
Generative Adversial Networks (GANs) have made a major impact in compute...
We study the effect of structural variation in graph data on the predict...
Latent variable models learn a stochastic embedding from a low-dimension...
We consider kernel methods on general geodesic metric spaces and provide...
Methodological contributions: This paper introduces a family of kernels ...
In order to develop statistical methods for shapes with a tree-structure...