A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
Macaw: A causal generative model for medical imaging
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
MuCALD-SplitFed adds causal-latent diffusion to multi-task split federated learning to raise segmentation accuracy and cut reconstruction and membership-inference leakage compared with standard SplitFed and personalized FL baselines.
citing papers explorer
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A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
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MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
MuCALD-SplitFed adds causal-latent diffusion to multi-task split federated learning to raise segmentation accuracy and cut reconstruction and membership-inference leakage compared with standard SplitFed and personalized FL baselines.