What question did this study set out to answer?

To review deep learning applications across major neuroimaging domains and establish guidelines for overcoming domain-specific data and modeling challenges.

February 24, 2022IEEE Signal Processing Magazine

Deep Learning in Neuroimaging: Promises and challenges

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To review deep learning applications across major neuroimaging domains and establish guidelines for overcoming domain-specific data and modeling challenges.
Categorized neuroimaging deep learning into four focus areas: classification/prediction, dynamic activity and connectivity, multimodal fusion, and interpretation/visualization.
Evaluated model architectures against neuroimaging constraints, including high dimensionality, small sample sizes, multimodal heterogeneity, and scarce ground truth.
Identified tailored network architectures and multimodal fusion strategies as essential methods to manage small sample sizes and complex image dimensions.
Established practical guidelines linking neuroimaging data characteristics directly to model design to improve interpretability and accelerate clinical deployment.

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Implication

Review demonstrates deep learning strategies across key neuroimaging categories, highlighting pathways to overcome high dimensionality and data scarcity in clinical translation.

Cite This Study

Yan et al. (2022) studied this question.

synapsesocial.com/papers/6a8590a102a1bc3ea31e65f7 https://doi.org/10.1109/msp.2021.3128348

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Implication

Review demonstrates deep learning strategies across key neuroimaging categories, highlighting pathways to overcome high dimensionality and data scarcity in clinical translation.

Deep Learning in Neuroimaging: Promises and challenges

Key Points

Discussion

Implication

Cite This Study

Deep Learning in Neuroimaging: Promises and challenges

Key Points

Discussion

Implication

Cite This Study

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