arXiv subjects

Sara Fin

Publications and source records attributed to Sara Fin.

3 recordsLinked to original sources

arXiv2026

Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Dementia Prediction

Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied almost entirely on a single scan, so the value of measuring anatomical change over time is largely untested. We ask what a second scan adds, under a strict evaluation: conversion is defined from recorded clinical diagnoses rather than enrolment category, the pretraining pool shares no participants with the evaluation cohort, a test partition is kept out of model development, and uncertainty is estimated by resampling participants, not scans. We introduce a temporal fusion network that combines paired scans in three ways (anatomical difference, cross-temporal attention, and joint context) and mixes the three with a learned per-patient gate. We compare it with single-scan and longitudinal baselines. A follow-up scan improves discrimination substantially, and a model with an unrelated architecture gains the same, so the benefit comes from temporal information, not from a particular design. How the scans are combined still matters: simple subtraction is no better than a single scan, while learned fusion recovers the full benefit. Two results count against the proposed method. It does not beat a simpler recurrent baseline in a comparison able to detect a small difference, and its adaptive gate, meant to explain individual predictions, is unstable across independently trained models and largely restates the prediction itself. Most of the improvement comes from the pretrained encoder, not the second timepoint, which points to a ceiling on what paired structural imaging can offer. The usual 0.5 threshold is also unsuitable at this prevalence: validation-chosen operating points change how clinically useful every model appears without changing any model. Further gains are more likely to come from richer inputs than from more elaborate fusion.

cs.CV↗

arXiv2026

Alzheimer's Disease Brain Network Mining

Machine learning approaches for Alzheimer's disease (AD) diagnosis face a fundamental challenges. Clinical assessments are expensive and invasive, leaving ground truth labels available for only a fraction of neuroimaging datasets. We introduce Multi view Adaptive Transport Clustering for Heterogeneous Alzheimer's Disease (MATCH-AD), a semi supervised framework that integrates deep representation learning, graph-based label propagation, and optimal transport theory to address this limitation. The framework leverages manifold structure in neuroimaging data to propagate diagnostic information from limited labeled samples to larger unlabeled populations, while using Wasserstein distances to quantify disease progression between cognitive states. Evaluated on nearly five thousand subjects from the National Alzheimer's Coordinating Center, encompassing structural MRI measurements from hundreds of brain regions, cerebrospinal fluid biomarkers, and clinical variables MATCHAD achieves near-perfect diagnostic accuracy despite ground truth labels for less than one-third of subjects. The framework substantially outperforms all baseline methods, achieving kappa indicating almost perfect agreement compared to weak agreement for the best baseline, a qualitative transformation in diagnostic reliability. Performance remains clinically useful even under severe label scarcity, and we provide theoretical convergence guarantees with proven bounds on label propagation error and transport stability. These results demonstrate that principled semi-supervised learning can unlock the diagnostic potential of the vast repositories of partially annotated neuroimaging data accumulating worldwide, substantially reducing annotation burden while maintaining accuracy suitable for clinical deployment.

cs.LG↗

arXiv2025

Dual Model Deep Learning for Alzheimer Prognostication

Disease modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal observations and provide no uncertainty quantification, rendering them impractical at the critical first visit when treatment decisions must be made. We developed PROGRESS (PRognostic Generalization from REsting Static Signatures), a dual-model deep learning framework that transforms a single baseline cerebrospinal fluid biomarker assessment into actionable prognostic estimates without requiring prior clinical history. The framework addresses two complementary clinical questions: a probabilistic trajectory network predicts individualized cognitive decline with calibrated uncertainty bounds achieving near-nominal coverage, enabling honest prognostic communication; and a deep survival model estimates time to conversion from mild cognitive impairment to dementia. Using data from over 3,000 participants across 43 Alzheimer's Disease Research Centers in the National Alzheimer's Coordinating Center database, PROGRESS substantially outperforms Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction. Risk stratification identifies patient groups with seven-fold differences in conversion rates, enabling clinically meaningful treatment prioritization. Leave-one-center-out validation demonstrates robust generalizability, with survival discrimination remaining strong across held-out sites despite heterogeneous measurement conditions spanning four decades of assay technologies. By combining superior survival prediction with trustworthy trajectory uncertainty quantification, PROGRESS bridges the gap between biomarker measurement and personalized clinical decision-making.

cs.LG↗