2025
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
Liu C, Xu K, Shen L, Huguet G, Wang Z, Tong A, Bzdok D, Stewart J, Wang J, Del Priore L, Krishnaswamy S. ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images. 2025, 00: 1-5. DOI: 10.1109/icassp49660.2025.10890535.Peer-Reviewed Original ResearchHigh-level visual featuresJoint representation spaceMedical image datasetsReal-world datasetsPreserving spatial detailsData sparsityImage datasetsVisual featuresUNet architectureMedical imaging technologyMultiscale representationLongitudinal medical imagesMedical imagesTrajectory smoothnessRepresentation spaceData scarcitySpatial detailsIrregular samplingMitigate data scarcityDatasetEmpirical validationImagesSparsityPrediction modelUNet
2024
Trust under development: The Italian validation of the Epistemic Trust, Mistrust, and Credulity Questionnaire (ETMCQ) for adolescents
Milesi A, Liotti M, Locati F, De Carli P, Speranza A, Campbell C, Fonagy P, Lingiardi V, Parolin L. Trust under development: The Italian validation of the Epistemic Trust, Mistrust, and Credulity Questionnaire (ETMCQ) for adolescents. PLOS ONE 2024, 19: e0307229. PMID: 39186540, PMCID: PMC11346731, DOI: 10.1371/journal.pone.0307229.Peer-Reviewed Original ResearchConceptsSelf-report instrumentEpistemic trustContext of personality disordersConcept of epistemic trustDevelopment of mental disordersPersonality disorderEmotion dysregulationMeasure mentalizingMental disordersPsychological functioningItalian validationMental healthPsychopathologyAdolescent populationInterpersonal trustAdolescentsDisordersResearch contextEmpirical validationHigh schoolItalian adult populationAdult populationMentalQuestionnaireValidityA causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns
Heffernan C, Koehler K, Zamora M, Buehler C, Gentner D, Peng R, Datta A. A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns. American Journal Of Epidemiology 2024, 194: 185-194. PMID: 38960671, PMCID: PMC11735973, DOI: 10.1093/aje/kwae171.Peer-Reviewed Original ResearchComparative interrupted time seriesAir pollutionCausal effectsAir pollution time seriesPollution time seriesTime seriesImpact of policy interventionsEnvironmental policyEastern USNatural experimentPolicy impactPolicy interventionsEmpirical validationPollutionIndustrial facilitiesFalse effectsBaseline yearNO2PolicyImpact of COVID-19Interrupted time series
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