Global-Local Transformer for Brain Age Estimation

He, Sheng; Grant, P. Ellen; Ou, Yangming

doi:10.1109/tmi.2021.3108910

Cited by 92 publications

(67 citation statements)

References 60 publications

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“…In these Nature, Cell, Lancet, JAMA, PNAS studies, initialisation with the benchmark model and refinement with samples in each specific AI task has led superior accuracies than training from scratch in specific tasks 104 122. In 3D medical images, we just trained a 3D benchmark AI model from 16 705 brain MRIs across the lifespan for predicting the continuously valued age 123 124. This offers one of the first benchmark AI models that can be transferred to small sample size studies such as in this SWS case.…”

Section: Methods and Analysismentioning

confidence: 99%

Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber

et al. 2022

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IntroductionSecondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000–50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers.Methods and analysisWe will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0–2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease.Ethics and disseminationThis study involves human participants and was approved by Boston Children’s Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children’s Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.

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Section: Methods and Analysismentioning

confidence: 99%

Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber

et al. 2022

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“…The CS is computed by: CS(α) = N |e|≤α /N × 100%, where N |e|≤α is the number of test pairs whose absolute error |e| is no higher than a given threshold α. Following previous works [37], [66], we set α = 5 (years) in experiments. The Pearson correlation is computed between the ground-truth r i and the estimated ri (i ∈ {1, 2, 3, 4}) on the whole test set.…”

Section: B Network Trainingmentioning

confidence: 99%

“…5) Accuracy comparison with state-of-the-art brain age estimation algorithms: Eight other deep learning based methods for brain age estimation are compared in this section, including the Hi-Net [39], FiA-Net [37], GL-Transformer [66], 3D CNN [4], SFCN [40], DeepBrainNet [9]. The Hi-Net [39] and FiA-Net [37] fuse the multi-channel input MRI images (intensity and RAVENS) in a layer-level fusion.…”

Section: Accuracy Evaluation For Brain Age Estimationmentioning

confidence: 99%

“…The Hi-Net [39] and FiA-Net [37] fuse the multi-channel input MRI images (intensity and RAVENS) in a layer-level fusion. The GL-Transformer [66] applies a global-local transformer for exploiting the global-context information from the whole image and the local fine-grained information from the local patches on 2D input images. The 3D CNN [4] uses 5 convolutional layers with kernel size of 3 × 3 × 3, followed by ReLU and max-pooling layers.…”

Section: Accuracy Evaluation For Brain Age Estimationmentioning

confidence: 99%

“…We also compare the accuracies of models with the relation learning (mSFCN+Transformer+Relation) and without relation learning (mSFCN+Transformer) where the model is directly trained to estimate the brain age. For the GL-Transformer [66], 3D CNN [4], SFCN [40], DeepBrain-Net [9] and mSFCN+Transformer, we train them from scratch with the same training configuration as our neural network for a fair comparison.…”

Section: Accuracy Evaluation For Brain Age Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

He¹,

Feng²,

Grant³

et al. 2022

Preprint

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Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four nonlinear relations are considered: "cumulative relation", "relative relation", "maximal relation" and "minimal relation". These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).

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Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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Global-Local Transformer for Brain Age Estimation

Cited by 92 publications

References 60 publications

Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber

Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Understanding the brain with attention: A survey of transformers in brain sciences

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