ArticleIEEE journal of biomedical and health informatics2021
Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning.Frontiers in artificial intelligence · 2025Review
- Longitudinal Alzheimer's Disease Progression Prediction With Modality Uncertainty and Optimization of Information Flow.IEEE journal of biomedical and health informatics · 2025Article
- Self-supervised learning of neighborhood embedding for longitudinal MRI.Medical image analysis · 2022Article
- Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI.IEEE transactions on medical imaging · 2022Article
- Multiple Instance Neuroimage Transformer.Predictive Intelligence in Medicine. PRIME (Workshop) · 2022Article
- Alcohol and the Adolescent Brain: What We've Learned and Where the Data Are Taking Us.Alcohol research : current reviews · 2022Review
- Longitudinal self-supervised learning.Medical image analysis · 2021Article
- Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models.Information processing in medical imaging : proceedings of the ... conference · 2021Article
- Self-Supervised Longitudinal Neighbourhood Embedding.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
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Authors and funding
7 authors.
Funding
Abstract
Many neurological diseases are characterized by gradual deterioration of brain structure andfunction. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In allthree experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.
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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.