Annotating medical imaging datasets is costly, so fine-tuning (or transf...
Statistical shape models (SSM) have been well-established as an excellen...
We introduce Point2SSM, a novel unsupervised learning approach that can
...
Statistical shape modeling is the computational process of discovering
s...
Statistical shape modeling (SSM) enables population-based quantitative
a...
Semantic segmentation is a critical step in automated image interpretati...
Statistical Shape Modeling (SSM) is a valuable tool for investigating an...
Clinical investigations of anatomy's structural changes over time could
...
Statistical shape modeling (SSM) is a valuable and powerful tool to gene...
The manifold assumption for high-dimensional data assumes that the data ...
Statistical shape modeling (SSM) directly from 3D medical images is an
u...
Statistical shape modeling is an essential tool for the quantitative ana...
In current biological and medical research, statistical shape modeling (...
Statistical shape modeling (SSM) characterizes anatomical variations in ...
Mapping data from and/or onto a known family of distributions has become...
Statistical shape modeling (SSM) has recently taken advantage of advance...
Unsupervised representation learning via generative modeling is a staple...
This paper addresses the ability of generative adversarial networks (GAN...
Deep networks are an integral part of the current machine learning parad...
Difficult image segmentation problems, for instance left atrium MRI, can...
Statistical shape modeling (SSM) has proven useful in many areas of biol...
Left atrium shape has been shown to be an independent predictor of recur...