arXiv:2603. 04113v2 Announce Type: replace-cross Abstract: Demographic attributes can be predicted from medical images, raising concerns about bias in clinical AI systems.
arXiv:2606. 17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis.
arXiv:2512. 08462v2 Announce Type: replace Abstract: Decoding brain states from functional magnetic resonance imaging (fMRI) data is vital for advancing neuroscience and clinical applications.
arXiv:2608.28787v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rec...
arXiv:2608. 08173v1 Announce Type: cross Abstract: Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease.
arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.
arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.
arXiv:2602. 18400v3 Announce Type: replace-cross Abstract: Missing data problems, such as missing modalities in multi-modal brain MRI and missing slices in cardiac MRI, pose significant challenges in clinical practice.
The paper presents a method for generating cardiac magnetic resonance (CMR) images conditioned on patient metadata using a pretrained latent diffusion model. By encoding structured clinical data and slice position as textual prompts and applying Metadata‑Free Classifier‑Free Guidance, Contrastive Batching, and Inverse‑Frequency Sampling, the authors improve the fidelity of synthetic images, achieving a 57% reduction in Fréchet Inception Distance compared to a baseline without these strategies. Evaluation on 59,058 UK Biobank CMR scans shows better distributional realism and subgroup alignment, though disease‑specific conditioning remains challenging.
The paper introduces MedIDL, a Medical Imaging Disentanglement Learning framework that separates disease-related features from confounding covariates and individual variability in medical images. It achieves this by projecting image features into three orthogonal latent spaces—disease classification, covariate alignment, and a Gaussian head for individual variation—using specialized disentanglement heads. Across seven diverse imaging datasets, MedIDL surpasses state‑of‑the‑art supervised and self‑supervised methods in classification accuracy, and its latent representations and gradient‑based visualizations align with known clinical patterns.
arXiv:2606. 18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization.
Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scenarios. While cross-modal knowledge distillation addresses this, existing methods often struggle with large modality gaps and the propagation of noise from uncertain source-domain predictions.