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This paper presents a quantum autoencoder for compression-driven anomaly detection in brain MRI, achieving high ROC-AUC scores and outperforming classical baselines while providing interpretable anomaly heatmaps.
This paper introduces Neuro-JEPA, a foundation model that uses a latent predictive objective and Mixture-of-Experts architecture to encode brain MRI scans across T1w, T2w, and FLAIR sequences, pretrained on a large dataset of 1.55 million scans.
This paper evaluates the open-weight LLM LLaMA 3.1 for automatic extraction of structured data from Dutch brain MRI reports, achieving high performance on visual rating scores and accurate detection of findings, with few-shot prompting improving extraction of numerical variables.
FlowLet is a conditional generative framework that synthesizes age-conditioned 3D brain MRIs using flow matching in an invertible wavelet domain, improving brain age prediction accuracy for underrepresented age groups with high efficiency.
WaveDiT is a conditional flow matching framework for full-resolution 3D brain MRI synthesis that operates in wavelet coefficient space, enabling efficient generation on standard GPUs without lossy latent compression. It achieves improved alignment with real MRI distributions and downstream tasks.