The paper introduces FORESEE-MS, a protocol for evaluating prospective spatial forecasting of future multiple sclerosis lesions using baseline FLAIR MRI. It compares a fold‑specific population‑prior atlas to five deep‑learning models (3D U‑Net, Attn+ResBlock+VAE, ViT, and two SwinUNETR variants) on voxel‑level discrimination (AUROC, AUPRC) and within‑patient lesion localization (Recall@Top‑K%). While the atlas achieved the best pooled AUROC and AUPRC, the 3D U‑Net and Attn+ResBlock+VAE showed higher within‑patient recall, though confidence intervals largely overlapped zero, leaving the benefit of patient‑specific predictive information uncertain in this small cohort.
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
arXiv:2609.23983v1 Announce Type: new Abstract: Neuroimaging foundation models pretrained on large, predominantly western cohorts are increasingly proposed as general-purpose backbones for brain MRI...
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
arXiv:2606. 03566v1 Announce Type: cross Abstract: Background: The lateral ventricle choroid plexus (LVCP) is gaining recognition as a key imaging biomarker for multiple sclerosis (MS) related to physical disability and neuroinflammation.
arXiv:2604.08015v3 Announce Type: replace-cross Abstract: Small lesions in brain MRI are hard to segment because they occupy a tiny fraction of the volume and are dominated by background and larger l...
The study introduces Spatial Atlas-Informed Loss Ensembles (SAILS) for whole-body PSMA PET/CT metastasis segmentation, integrating a population metastasis atlas into nnU-Net training via voxel-wise mean and standard deviation maps. SAILS improves lesion detection, notably in high-risk bone regions, achieving higher Dice scores and sensitivity on AutoPET~III and Deep-PSMA datasets compared to baseline methods, though it also increases false-positive volume. The approach eliminates the need for atlas registration at inference time.
arXiv:2609.09424v1 Announce Type: new Abstract: Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consistent insta...
The paper introduces Neuro‑JEPA, a sparse multimodal foundation model that learns unified representations of brain MRI across T1w, T2w, and FLAIR sequences using a latent predictive objective and a Mixture‑of‑Experts architecture. It was pretrained on over 1.5 million scans from 428,647 studies and systematically evaluates architectural, masking, objective, and sparsity choices for robust multimodal representation learning. Across 47 tasks from three health systems and 12 public datasets, Neuro‑JEPA consistently outperforms a simple CNN baseline, demonstrating its effectiveness for both clinical and research applications.
arXiv:2610.35396v1 Announce Type: new Abstract: We evaluated synthetic pretraining and multi-cohort transfer learning for deep learning-based super-resolution of intracranial 4D Flow MRI across heter...
VariaFlow is an uncertainty‑aware flow‑matching framework for unsupervised anomaly detection in brain MRI. It generates high‑quality pseudo‑healthy reconstructions and estimates a predictive variance via an ensemble of mask‑guided restorations, normalizing residuals to distinguish genuine pathology from normal anatomical variation. Trained once on healthy data, VariaFlow achieves state‑of‑the‑art AUROC and AUPRC across stroke and tumor datasets, outperforming baselines by up to 50% and maintaining stability under domain shift.
The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.
Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is difficult because lesions are subtle and share intensi...