Search Results for author:
Found 7 papers, 2 papers with code
Multimodal and multicontrast image fusion via deep generative models
As proof of concept, we test our architecture on the well characterized Human Connectome Project database demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to different phenotypical information which was not included in the embedding creation process.
DBGDGM: Dynamic Brain Graph Deep Generative Model
In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
VAESim: A probabilistic approach for self-supervised prototype discovery
Then, we reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors of the clusters.
Contrastive learning for unsupervised medical image clustering and reconstruction
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials.
GRADE: Graph Dynamic Embedding
At each time step link generation is performed by first assigning node membership from a distribution over the communities, and then sampling a neighbor from a distribution over the nodes for the assigned community.
RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow
A novel method to identify salient computational paths within randomly wired neural networks before training is proposed.
Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis and Decision Making
Modern medicine requires generalised approaches to the synthesis and integration of multimodal data, often at different biological scales, that can be applied to a variety of evidence structures, such as complex disease analyses and epidemiological models.
