2025
Texture and noise dual adaptation for infrared image super-resolution
Huang Y, Miyazaki T, Liu X, Dong Y, Omachi S. Texture and noise dual adaptation for infrared image super-resolution. Pattern Recognition 2025, 163: 111449. DOI: 10.1016/j.patcog.2025.111449.Peer-Reviewed Original ResearchTexture detailsAdversarial lossSuper-resolutionInfrared image super-resolutionVisible imagesImage super-resolutionState-of-the-artIR image qualityVisible light imagesAdversarial trainingExtraction branchUpsampling factorsBlurring artifactsImage processingModel adaptationAdaptive approachSpatial domainImage qualityNoiseInnovation frameworkLight imagesNoise transferDual adaptationImagesTexture distributionT‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment
Kyro G, Smaldone A, Shee Y, Xu C, Batista V. T‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment. Journal Of Chemical Information And Modeling 2025, 65: 2395-2415. PMID: 39965912, DOI: 10.1021/acs.jcim.4c02332.Peer-Reviewed Original ResearchConceptsProtein-ligand binding affinity predictionBinding affinity predictionState-of-the-art performanceTransformer-based deep neural networksMultimodal feature representationAffinity predictionBinding affinity of small moleculesState-of-the-artDeep neural networksDeep learning modelsAffinity of small moleculesSelf-learning methodSARS-CoV-2 main proteasePredicted binding affinitiesFeature representationBinding affinityOn-target potencyNeural networkDrug discovery applicationsTransformation frameworkLearning modelsScoring functionCrystal structureSelf-learningMain proteaseClassifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study
Cardamone N, Olfson M, Schmutte T, Ungar L, Liu T, Cullen S, Williams N, Marcus S. Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study. JMIR Medical Informatics 2025, 13: e65454. PMID: 39864953, PMCID: PMC11884378, DOI: 10.2196/65454.Peer-Reviewed Original ResearchConceptsElectronic health recordsMental health termsHealth termsClinical expertsEmergency departmentHealth recordsPhysical healthMental healthElectronic health record systemsHealth care provider organizationsMental health-related problemsElectronic health record data setsCare provider organizationsMental health cliniciansMental health disordersHealth-related problemsED episodesHealth cliniciansRecords of individualsState-of-the-artDiagnostic codesHealth disordersHospital readmissionProvider organizationsMortality riskBiomedRAG: A retrieval augmented large language model for biomedicine
Li M, Kilicoglu H, Xu H, Zhang R. BiomedRAG: A retrieval augmented large language model for biomedicine. Journal Of Biomedical Informatics 2025, 162: 104769. PMID: 39814274, PMCID: PMC11837810, DOI: 10.1016/j.jbi.2024.104769.Peer-Reviewed Original Research
2024
Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach
Lee J, Murthy D, Ouellette R, Anand T, Kong G. Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach. Substance Use & Misuse 2024, 60: 677-683. PMID: 40019898, PMCID: PMC11871408, DOI: 10.1080/10826084.2024.2447415.Peer-Reviewed Original ResearchConceptsImage clustering approachImage clustering modelsState-of-the-artE-cigarette-related contentMachine learning approachSocial media dataImage clusteringAnalysis of visual dataSocial media platformsVisual dataLearning approachMedia dataClustering approachMedia platformsImage-based social media platformsQualitative evaluationSocial mediaImage-based analysisImagesTikTokCluster modelUnsupervisedVideoClustersCloudStyle mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation
Cai Z, Xin J, You C, Shi P, Dong S, Dvornek N, Zheng N, Duncan J. Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation. Medical Image Analysis 2024, 101: 103440. PMID: 39764933, DOI: 10.1016/j.media.2024.103440.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationMedical image segmentationDomain-invariant representationsImage segmentationDomain adaptationDisentanglement learningImage translationUnsupervised domain adaptation approachState-of-the-art methodsDomain shift problemDomain-invariant learningState-of-the-artPublic cardiac datasetsDiverse constraintsAdversarial learningConsistency regularizationContrastive learningFeature spaceSemantic consistencyComprehensive experimentsDomain generalizationData diversityShift problemMedical segmentationCardiac datasetsImproved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
Lee H, Emani P, Gerstein M. Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling. Journal Of Chemical Information And Modeling 2024, 64: 8684-8704. PMID: 39576762, PMCID: PMC11632770, DOI: 10.1021/acs.jcim.4c01116.Peer-Reviewed Original ResearchBinding affinity predictionAffinity predictionMeta-modelMeta-modeling approachLigand-protein binding affinityState-of-the-art deep learning toolsState-of-the-artBinding affinityDeep learning modelsDeep learning toolsMolecular descriptorsInclusion of featuresVirtual screeningBase modelDatabase scalabilityGeneralization capabilityDiverse modeling approachesTraining databaseApplication benchmarksDrug ligandsLearning modelsLigandPhysicochemical propertiesLearning toolsDevelopment effortsGENCODE 2025: reference gene annotation for human and mouse
Mudge J, Carbonell-Sala S, Diekhans M, Martinez J, Hunt T, Jungreis I, Loveland J, Arnan C, Barnes I, Bennett R, Berry A, Bignell A, Cerdán-Vélez D, Cochran K, Cortés L, Davidson C, Donaldson S, Dursun C, Fatima R, Hardy M, Hebbar P, Hollis Z, James B, Jiang Y, Johnson R, Kaur G, Kay M, Mangan R, Maquedano M, Gómez L, Mathlouthi N, Merritt R, Ni P, Palumbo E, Perteghella T, Pozo F, Raj S, Sisu C, Steed E, Sumathipala D, Suner M, Uszczynska-Ratajczak B, Wass E, Yang Y, Zhang D, Finn R, Gerstein M, Guigó R, Hubbard T, Kellis M, Kundaje A, Paten B, Tress M, Birney E, Martin F, Frankish A. GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Research 2024, 53: d966-d975. PMID: 39565199, PMCID: PMC11701607, DOI: 10.1093/nar/gkae1078.Peer-Reviewed Original ResearchGene annotationLong-read transcriptome sequencingMulti-genome alignmentsRibo-Seq experimentsUCSC Genome BrowserState-of-the-art proteomicsGenome browserRibo-seqSpecies genomesMouse genomeTranscriptome sequencingGENCODEGenomeAnnotation workflowAnnotationSequencePangenomeMiceGenesetsState-of-the-artUCSCProteomicsTranscriptionGenesSpeciesImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsFlat Panel TOF-PET Detectors: a Simulation Study
Orehar M, Dolenec R, Fakhri G, Gascón D, Gola A, Korpar S, Križan P, Razdevšek G, Marin T, Chemli Y, Žontar D, Pestotnik R. Flat Panel TOF-PET Detectors: a Simulation Study. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658250.Peer-Reviewed Original ResearchTime resolutionAngular coverageFlat-panel detectorScintillation materialsGATE softwareAxial coverageBiograph VisionPanel detectorTotal-body coverageClinical scannerImage reconstructionDetectorReconstructed imagesHomogeneous contrastCylindrical scannerImage qualityState-of-the-artScintillationHigh-performance computingScannerPhantomResolutionCore hoursPositron emission tomographyGate2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC
Chen T, Hou J, Xie H, Chen X, Zhou Y, Xia M, Duncan J, Liu C, Zhou B. 2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less AC. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10658551.Peer-Reviewed Original ResearchLow-dose PETStandard-dose PETImage-to-image translationPositron emission tomographyAttenuation correctionPET reconstructionOverall radiation doseCT acquisitionState-of-the-art deep learning methodsRadiation hazardRadiation doseCNN-based methodsState-of-the-artMedical image translationPatient studiesDiffusion modelDeep learning methodsHigh computation costHuman patient studiesClinical imaging toolImage translationBaseline methodsMulti-viewCNN-basedMultiple viewsA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang G, Warrell J, Zheng S, Gerstein M. A variational graph-partitioning approach to modeling protein liquid-liquid phase separation. Cell Reports Physical Science 2024, 5: 102292. PMID: 39866853, PMCID: PMC11760192, DOI: 10.1016/j.xcrp.2024.102292.Peer-Reviewed Original ResearchPatient-Centered and Practical Privacy to Support AI for Healthcare
Liu R, Lee H, Bhavani S, Jiang X, Ohno-Machado L, Xiong L. Patient-Centered and Practical Privacy to Support AI for Healthcare. 2024, 00: 265-272. DOI: 10.1109/tps-isa62245.2024.00038.Peer-Reviewed Original ResearchDifferential privacyArtificial intelligenceState-of-the-art approachesPrivacy-sensitive domainsState-of-the-artSensitive patient informationIntegration of artificial intelligenceClinical decision supportPrivacy requirementsPrivacy guaranteesPrivacy solutionsPractical privacyPotential research directionsPrivacy concernsVision paperPrivacy needsAI systemsAI modelsPrivacyDecision supportResearch directionsPatient informationTrade-offsModel's utilityPrediction modelPDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation
Zhou B, Chen T, Hou J, Zhou Y, Xie H, Liu C, Duncan J. PDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation. Lecture Notes In Computer Science 2024, 15241: 259-268. DOI: 10.1007/978-3-031-73284-3_26.Peer-Reviewed Original ResearchEfficient plug-and-play moduleDenoising diffusion probabilistic modelPlug-and-play moduleDiffusion probabilistic modelState-of-the-artMedical image analysisDeep modelsSegmentation datasetUncertainty estimationSegmentation resultsImproved segmentationSegmentation modelMorphological operationsBinary segmentationSegmentation improvementProbabilistic modelUncertainty mapsDiffusion moduleReverse pathPerturbation segmentsSegmental inputsImage analysisSegmentsInputModulationImproving tabular data extraction in scanned laboratory reports using deep learning models
Li Y, Wei Q, Chen X, Li J, Tao C, Xu H. Improving tabular data extraction in scanned laboratory reports using deep learning models. Journal Of Biomedical Informatics 2024, 159: 104735. PMID: 39393477, DOI: 10.1016/j.jbi.2024.104735.Peer-Reviewed Original ResearchTree edit distanceOptical character recognitionTable recognitionDeep learning modelsAverage recallAverage precisionState-of-the-art deep learning modelsLearning modelsRegion-of-interest detectionState-of-the-artCharacter recognitionDetection evaluationTree editingTabular dataImpressive resultsLab test resultsLaboratory test reportsClinical documentationRecognitionLaboratory reportsHealthcare organizationsClinical data analysisDecision makingClinical decision makingTest reportsClass-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation
Cai Z, Xin J, Zeng T, Dong S, Zheng N, Duncan J. Class-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation. Lecture Notes In Computer Science 2024, 15008: 68-79. DOI: 10.1007/978-3-031-72111-3_7.Peer-Reviewed Original ResearchSemi-supervised domain adaptationCross-domain segmentationTail classesBridge the domain gapState-of-the-art methodsMean-teacher modelUnlabeled target samplesLabeled source samplesState-of-the-artDomain gapDomain adaptationKnowledge distillationMixup strategyIntra-domainTarget domainEnhance model performanceMM-WHSData distributionSegmentation performanceTarget samplesMixupMS-CMRSegConsistency alignmentClass awarenessExperimental resultsExploring Spatio-temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis
Li L, Zhang L, Cao P, Yang J, Wang F, Zaiane O. Exploring Spatio-temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis. Lecture Notes In Computer Science 2024, 15002: 195-205. DOI: 10.1007/978-3-031-72069-7_19.Peer-Reviewed Original ResearchBrain functional modulesState-of-the-art performanceMajor depressive disorderTransformer-based frameworkSelf-attention mechanismState-of-the-artEnd-to-endSpatio-temporal representationBrain disorder diagnosisBipolar disorderBrain disordersDiagnosis of major depressive disorderPatterns of brain activityClustering strategyDynamic brain functionAssociated with brain disordersDepressive disorderExperimental resultsDisorder diagnosisBrain activitySpatio-temporal characteristicsBrain functionFunctional modulesDisordersSpatio-temporal patternsPredicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song T, Cosatto E, Wang G, Kuang R, Gerstein M, Min M, Warrell J. Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs. Bioinformatics 2024, 40: ii111-ii119. PMID: 39230702, PMCID: PMC11373608, DOI: 10.1093/bioinformatics/btae383.Peer-Reviewed Original ResearchConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkThe United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection
Buchsbaum J, Capala J, Obcemea C, Keppel C, Asai M, Chen G, Christy M, Fakhri G, Gueye P, Pogue B, Ruckman L, Tourassi G, Vetter K, Zhao W, Squires A, Saboury B, Wang G, Domurat‐Sousa K, Weisenberger A. The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection. Medical Physics 2024, 51: 8654-8669. PMID: 39177300, PMCID: PMC11659064, DOI: 10.1002/mp.17333.Peer-Reviewed Original ResearchNational Institutes of HealthState-of-the-artApplication of artificial intelligenceDOE Office of ScienceArtificial intelligenceMedical care advancesImage reconstructionIn-person workshopsOffice of ScienceRadiation detectionHealth collaborationInstitutes of HealthCare advancesIn-personAreas of successCascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Zhou Y, Chen T, Hou J, Xie H, Dvornek N, Zhou S, Wilson D, Duncan J, Liu C, Zhou B. Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation. Medical Image Analysis 2024, 98: 103300. PMID: 39226710, DOI: 10.1016/j.media.2024.103300.Peer-Reviewed Original ResearchGenerative adversarial networkMedical image translationImage translationState-of-the-art methodsImage-to-image translationMedical image datasetsImage translation tasksImage-to-imageState-of-the-artMedical image processingHigh-quality translationsUncertainty estimationCascaded pipelineAdversarial networkImage datasetsSub-tasksTranslation qualityTranslation performanceTranslation tasksImage processingTranslation resultsDM methodPrior imageRobust performanceExperimental results
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