ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE transactions on neural networks and learning systems · 2024Article
- Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.Abdominal radiology (New York) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Diffusion MRI-derived brain structural connectomes or brain networks are widely used in the brain research. However, constructing brain networks is highly dependent on various tractography algorithms, which leads to difficulties in deciding the optimal view concerning the downstream analysis. In this paper, we propose to learn a unified representation from multi-view brain networks. Particularly, we expect the learned representations to convey the information from different views fairly and in a disentangled sense. We achieve the disentanglement via an approach using unsupervised variational graph auto-encoders. We achieve the view-wise fairness,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.