ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Deep Representation Learning For Multimodal Brain Networks.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE transactions on neural networks and learning systems · 2024Article
- Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.Human brain mapping · 2023Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Bidirectional Mapping with Contrastive Learning on Multimodal Neuroimaging Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Chromatic fusion: generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.medRxiv : the preprint server for health sciences · 2023Article
- Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Signed graph representation learning for functional-to-structural brain network mapping.Medical image analysis · 2023Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- A Hierarchical Graph Learning Model for Brain Network Regression Analysis.Frontiers in neuroscience · 2022Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks - DMBN) to fuse multimodal brain networks. Specifically, we decipher the cross-modality relationship through a graph encoding and decoding process. The higher-order network mappings from brain structural networks to functional networks are learned in the node domain. The learned network representation is a set of node features that are informative to induce brain saliency maps in a supervised manner. We test our framework in both synthetic and real image data. The experimental results show the superiority of the proposed method over some other state-of-the-art deep brain network models.
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What OpenQuestion holds
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.