TAF-Net adaptively fuses longitudinal structural MRI via a temporal gate to achieve top performance in 3-year MCI-to-AD conversion prediction on ADNI using only MRI.
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A 4D diffusion generative model learns topology-preserving spatiotemporal deformations to synthesize realistic longitudinal brain anatomy trajectories in neurodegenerative diseases from sparse follow-up scans.
SemanticVessel is a new CTA vessel segmentation dataset using intensity-guided region growing, expert labeling of 20 arterial classes, and multi-phase label reuse, with reported gains from including a generic minor-artery class.
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
Deep learning on pairwise comparisons detects temporal changes in 0.35T MR-Linac images during prostate radiotherapy with AUC 0.99 for first-to-last fraction pairs.
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.
citing papers explorer
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Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction
TAF-Net adaptively fuses longitudinal structural MRI via a temporal gate to achieve top performance in 3-year MCI-to-AD conversion prediction on ADNI using only MRI.
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Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
A 4D diffusion generative model learns topology-preserving spatiotemporal deformations to synthesize realistic longitudinal brain anatomy trajectories in neurodegenerative diseases from sparse follow-up scans.
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Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
SemanticVessel is a new CTA vessel segmentation dataset using intensity-guided region growing, expert labeling of 20 arterial classes, and multi-phase label reuse, with reported gains from including a generic minor-artery class.
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Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis
SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.
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AI-Based Detection of Temporal Changes in MR-Linac Images Acquired During Routine Prostate Radiotherapy
Deep learning on pairwise comparisons detects temporal changes in 0.35T MR-Linac images during prostate radiotherapy with AUC 0.99 for first-to-last fraction pairs.
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Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
Post-hoc normalizing flows for OOD detection in medical imaging achieve 84.61% AUROC on MedOOD and 93.8% on MedMNIST, outperforming ViM, MDS, and ReAct.