arXiv:2511.14588v1 Announce Type: new 
Abstract: White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation have advanced, most provide only global lesion load and overlook their spatial distribution across distinct white matter regions. We propose a deep learning framework for robust WMH segmentation and localization, evaluated across public datasets and an independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our results show that the predicted lesion loads are in line with the reference WMH estimates, confirming the robustness to variations in lesion load, acquisition, and demographics. Beyond accurate segmentation, we quantify WMH load within anatomically defined regions and combine these measures with brain structure volumes to assess diagnostic value. Regional WMH volumes consistently outperform global lesion burden for disease classification, and integration with brain atrophy metrics further improves performance, reaching area under the curve (AUC) values up to 0.97. Several spatially distinct regions, particularly within anterior white matter tracts, are reproducibly associated with diagnostic status, indicating localized vulnerability in AD. These results highlight the added value of regional WMH quantification. Incorporating localized lesion metrics alongside atrophy markers may enhance early diagnosis and stratification in neurodegenerative disorders.

تُعتبر الزيادة في كثافة المادة البيضاء (WMH) علامات تصويرية رئيسية في الشيخوخة الإدراكية ومرض الزهايمر (AD). على الرغم من التقدم في الأساليب الآلية لتجزئة WMH، فإن معظمها يوفر فقط عبء آفة عالمي ويتجاهل توزيعها المكاني عبر مناطق المادة البيضاء المختلفة. تم اقتراح إطار تعلم عميق لتجزئة WMH وتحديد موقعها بشكل موثوق، تم تقييمه عبر مجموعات بيانات عامة وعيّنة مستقلة من مبادرة تصوير مرض الزهايمر العصبي (ADNI). تُظهر النتائج أن أحمال الآفات المتوقعة تتماشى مع تقديرات WMH المرجعية، مما يؤكد القوة في مواجهة التغيرات في عبء الآفة، والاكتساب، والديموغرافيا.

Las hiperintensidades de la materia blanca (WMH) son marcadores de imagen clave en el envejecimiento cognitivo y la enfermedad de Alzheimer (EA). A pesar de los avances en los métodos automatizados para la segmentación de WMH, la mayoría solo proporcionan una carga de lesión global y pasan por alto su distribución espacial en distintas regiones de la materia blanca. Se propone un nuevo marco de aprendizaje profundo para la segmentación y localización robustas de WMH, evaluado en conjuntos de datos públicos y en una cohorte independiente de la Iniciativa de Neuroimagen de la Enfermedad de Alzhe…

Les hyperintensités de la matière blanche (WMH) sont des marqueurs d'imagerie importants dans le vieillissement cognitif et la maladie d'Alzheimer (MA). Malgré les avancées des méthodes automatisées de segmentation des WMH, la plupart ne fournissent qu'une charge lésionnelle globale et négligent la distribution spatiale dans les régions de matière blanche. Un nouveau cadre d'apprentissage profond a été proposé pour la segmentation et la localisation robustes des WMH, validé sur des ensembles de données publics et un cohort indépendant de l'Initiative d'Imagerie Neurodégénérative de la Maladie …

White matter hyperintensities (WMH) are significant imaging markers in cognitive aging and Alzheimer's disease (AD). Despite advancements in automated WMH segmentation, most methods only provide global lesion load and neglect spatial distribution across white matter regions. A new deep learning framework has been proposed for robust WMH segmentation and localization, validated on public datasets and an independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The study indicates that regional WMH volumes are more effective than global lesion burden for disease classification.

Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease

arXiv:2512.03475v2 Announce Type: replace 
Abstract: Event-based models (EBMs) infer disease progression from cross-sectional data, and standard EBMs assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model (JPM), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several JPM variants (Pairwise, Bradley-Terry, Plackett-Luce, and Mallows) and analyze three properties: (i) calibration -- whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation -- the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness -- the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, JPM improves ordering accuracy by roughly 21 percent over a strong EBM baseline (SA-EBM) that treats the joint disease as a single condition. Finally, using NACC, we find that the Mallows variant of JPM and the baseline model (SA-EBM) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of AD and VaD.

تم تقديم نموذج التقدم المشترك (JPM) كإطار احتمالي لتحليل تقدم الأمراض المختلطة في الأمراض التنكسية العصبية، حيث يعالج قيود النماذج المعتمدة على الأحداث التي تفترض عادةً وجود مرض واحد فقط لكل فرد. يتضمن هذا النموذج عدة متغيرات لتعزيز فهم مسارات المرض.

Se ha introducido el Modelo de Progresión Conjunta (JPM) como un marco probabilístico para analizar la progresión de patologías mixtas en enfermedades neurodegenerativas, abordando las limitaciones de los modelos basados en eventos que generalmente suponen una sola enfermedad por individuo. Este modelo incorpora varias variantes para mejorar la comprensión de las trayectorias de la enfermedad.

Le Joint Progression Model (JPM) a été introduit comme un cadre probabiliste pour analyser la progression des pathologies mixtes dans les maladies neurodégénératives, abordant les limitations des modèles basés sur des événements qui supposent généralement une seule maladie par individu. Ce modèle intègre diverses variantes pour améliorer la compréhension des trajectoires de la maladie.

The Joint Progression Model (JPM) has been introduced as a probabilistic framework for analyzing mixed-pathology progression in neurodegenerative diseases, addressing the limitations of traditional event-based models that typically assume a single disease per individual. This model incorporates various variants to enhance the understanding of disease trajectories.

Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression

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Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease

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