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.
By Maryam Akhavan Aghdam, Sudip Mittal, Noorbakhsh Amiri Golilarz, Shahram Rahimi
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.
By Po-Jui Lu, Alessandro Cagol, Mario Ocampo-Pineda, Federico Spagnolo, Marina Mastantuono, Andreea-Alexandra Aldea, Jannis M\"uller, \"Ozg\"ur Yaldizli, Matthias Weigel, Lester Melie-Garcia, Roberta Magliozzi, Maria Pia Sormani, Ludwig Kappos, Jens Kuhle, Cristina Granziera
arXiv:2610.08848v1 Announce Type: cross
Abstract: Glioblastoma (GBM), an aggressive primary brain tumor, is routinely monitored with longitudinal MRI after treatment. Distinguishing stable disease (S...
By Wenhao Guo, Changchang Yin, Pierre Giglio, Weidan Cao, Ping Zhang, Golrokh Mirzaei
DiaSeg extracts diagonal segments from Dynamic Time Warping (DTW) paths, characterizing each with five geometric features to preserve local alignment information. In a study of 91 subjects across six clinical conditions, these segments revealed consistent unsupervised patterns aligned with biomechanical phases and achieved near-perfect separation of healthy and pathological gait. While cycle‑based methods reached higher overall accuracy, DiaSeg offers phase‑specific interpretability, pinpointing where coordination breaks down within the gait cycle.
By Tresor Y. Koffi, Amel Hidouri, Corentin Legrand, Aur\'elie Bertaux
arXiv:2606. 08897v1 Announce Type: cross Abstract: Spinal pathology is a leading cause of pain and disability worldwide.
By Zhiping Xiao, Junwei Yang, Gongbo Sun, Han Zhang, Hanwen Xu, Yi Yao, Zachary D. Miller, William E. King III, Mohammed M. Kanani, Jalal B. Andre, Sammy Chu, Ming Zhang, Paul E. Kinahan, Nathan M. Cross, Sheng Wang
arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).
By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez
arXiv:2607. 19696v1 Announce Type: cross Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks.
By Duong Ngoc Vu, Hai Son Nguyen, Trong-Nghia Nguyen, Bien Tran Van, Trang Mai Xuan, Huan Vu, Thien Van Luong
arXiv:2608.20969v1 Announce Type: new
Abstract: Multimodal clinical AI is limited by weakly aligned inputs and the absence of domain-specific interpretable representations, particularly when learning...
By Chen Dong, He Zonglin, Cheung Kenneth M. C
GateSPINE is a vision‑language framework designed for automated lumbar spine MRI report generation. It fuses sagittal T1 and T2 volumes using a training‑free gated cross‑view fusion module, then encodes the fused sagittal and axial volumes with parallel 3D encoders before decoding the combined representation into a report. Evaluated on three datasets, GateSPINE achieves the highest clinical efficacy F1 scores, improving recall across all datasets while remaining competitive on standard natural language generation metrics.
By Hoang Nguyen Van, Cuong Vuong Tuan, Trang Mai Xuan, Bien Tran Van, Nam Tran Van, Thien Van Luong
The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse, high-quality benchmarks. In this study, we present PhenSPINE, a Magnetic Resonance Imaging dataset comprising 16,813 images from 250 patients, curated to facilitate advanced deep learning research.
arXiv:2608. 16122v1 Announce Type: cross Abstract: Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity in adolescents that, if left untreated, can result in severe health outcomes.
By Dong Chen, Kenneth M. C. Cheung
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...
By Minh Sao Khue Luu, Evgeniy N. Pavlovskiy, Bair N. Tuchinov