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Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model
The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-processing and segmentation involved in traditional PDM models.
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models
Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images.
Review Regularized Neural Collaborative Filtering
In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution.
Unsupervised Shape Normality Metric for Severity Quantification
The severity of an anatomical deformity often serves as a determinant in the clinical management of patients.
