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Time Series Generative Learning with Application to Brain Imaging Analysis

arXiv 2407.14003 ↗First posted 19 Jul 2024stat.ML cs.LG eess.IVcited by 0

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Abstract (arXiv)

This paper focuses on the analysis of sequential image data, particularly brain imaging data such as MRI, fMRI, CT, with the motivation of understanding the brain aging process and neurodegenerative diseases. To achieve this goal, we investigate image generation in a time series context. Specifically, we formulate a min-max problem derived from the f-divergence between neighboring pairs to learn a time series generator in a nonparametric manner. The generator enables us to generate future images by transforming prior lag-k observations and a random vector from a reference distribution. With a deep neural network learned generator, we prove that the joint distribution of the generated sequence converges to the latent truth under a Markov and a conditional invariance condition. Furthermore, we extend our generation mechanism to a panel data scenario to accommodate multiple samples. The effectiveness of our mechanism is evaluated by generating real brain MRI sequences from the Alzheimer's Disease Neuroimaging Initiative. These generated image sequences can be used as data augmentation to enhance the performance of further downstream tasks, such as Alzheimer's disease detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 19 Jul 2024.

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References · 27

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15 more
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