An Ensemble Deep Learning Approach Combining Phenotypic Data and fMRI for ADHD Diagnosis

Qin, Yuanze; Lou, Yiwei; Huang, Yu; Chen, Rigao; Yue, Weihua

doi:10.1007/s11265-022-01812-0

Cited by 17 publications

(1 citation statement)

References 52 publications

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“…The transformer was first proposed in (Vaswani et al 2017) for machine translation, and since then, it has been widely applied to various tasks in the field of natural language processing and extended for computer vision tasks (Qin et al 2022;Xu et al 2023). A popular direction explores adopting transformer to multimodal feature fusion.…”

Section: Transformer For Feature Fusionmentioning

confidence: 99%

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

Zhang,

Lou,

et al. 2024

AAAI

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The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

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Section: Transformer For Feature Fusionmentioning

confidence: 99%