ArticleIEEE transactions on medical imaging2019
Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder.IEEE transactions on medical imaging · 2025Article
- Inferring neurocognition using artificial intelligence on brain MRIs.Frontiers in neuroimaging · 2024Review
- Prediction of Infant Cognitive Development with Cortical Surface-Based Multimodal Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.Frontiers in aging neuroscience · 2023Article
- Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores.IEEE transactions on medical imaging · 2022Article
- DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network.Frontiers in neuroinformatics · 2021Article
- Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cognitive scores. To this end, we introduce a multi-view multi-task learning approach to intuitively explore complementary information from different time-points and handle the missing data issue in longitudinal studies simultaneously. Accordingly, we establish a novel model, latent partial multi-view representation learning. Our approach regards data from different time-points as different views and constructs a latent representation to capture the complementary information from incomplete time-points. The latent representation explores the complementarity across different time-points and improves the accuracy of prediction. The minimization problem is solved by the alternating direction method of multipliers. Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.
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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.