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 distributionLearning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain
Kim H, Karaman B, Zhao Q, Wang A, Sabuncu M, Initiative F, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Landau S, Rivera-Mindt M, Okonkwo O, Shaw L, Lee E, Toga A, Beckett L, Harvey D, Green R, Saykin A, Nho K, Perrin R, Tosun D, Sachdev P, Green R, Drake E, Montine T, Conti C, Weiner M, Nosheny R, Sacrey D, Fockler J, Miller M, Kwang W, Jin C, Diaz A, Ashford M, Flenniken D, Kormos A, Petersen R, Aisen P, Rafii M, Raman R, Jimenez G, Donohue M, Salazar J, Fidell A, Boatwright V, Robison J, Zimmerman C, Cabrera Y, Walter S, Clanton T, Shaffer E, Webb C, Hergesheimer L, Smith S, Ogwang S, Adegoke O, Mahboubi P, Pizzola J, Jenkins C, Beckett L, Harvey D, Donohue M, Saito N, Diaz A, Hussen K, Okonkwo O, Rivera-Mindt M, Amaza H, Thao M, Parkins S, Ayo O, Glittenberg M, Hoang I, Germano K, Strong J, Weisensel T, Magana F, Thomas L, Guzman V, Ajayi A, Di Benedetto J, Talavera S, Jack C, Felmlee J, Fox N, Thompson P, DeCarli C, Forghanian-Arani A, Borowski B, Reyes C, Hedberg C, Ward C, Schwarz C, Reyes D, Gunter J, Moore-Weiss J, Kantarci K, Matoush L, Senjem M, Vemuri P, Reid R, Malone I, Thomopoulos S, Nir T, Jahanshad N, Knaack A, Fletcher E, Harvey D, Tosun-Turgut D, Chen S, Choe M, Crawford K, Yushkevich P, Das S, Jagust W, Landau S, Koeppe R, Rabinovici G, Villemagne V, LoPresti B, Perrin R, Morris J, Franklin E, Bernhardt H, Cairns N, Taylor-Reinwald L, Shaw L, Lee E, Lee V, Korecka M, Brylska M, Wan Y, Trojanowki J, Toga A, Crawford K, Neu S, Saykin A, Nho K, Foroud T, Jo T, Risacher S, Craft H, Apostolova L, Nudelman K, Faber K, Potter Z, Lacy K, Kaddurah-Daouk R, Shen L, Karlawish J, Erickson C, Grill J, Largent E, Harkins K, Weiner M, Thal L, Kachaturian Z, Frank R, Snyder P, Buckholtz N, Hsiao J, Ryan L, Molchan S, Khachaturian Z, Carrillo M, Potter W, Barnes L, Bernard M, González H, Ho C, Hsiao J, Jackson J, Masliah E, Masterman D, Okonkwo O, Perrin R, Ryan L, Silverberg N, Silbert L, Kaye J, White S, Pierce A, Thomas A, Clay T, Schwartz D, Devereux G, Taylor J, Ryan J, Nguyen M, DeCapo M, Shang Y, Schneider L, Munoz C, Ferman D, Conant C, Martin K, Oleary K, Pawluczyk S, Trejo E, Dagerman K, Teodoro L, Becerra M, Fairooz M, Garrison S, Boudreau J, Avila Y, Brewer J, Jacobson A, Gama A, Kim C, Little E, Frascino J, Ferng N, Trujillo S, Heidebrink J, Koeppe R, MacDonald S, Malyarenko D, Ziolkowski J, O’Connor J, Robert N, Lowe S, Rogers V, Clinic M, Petersen R, Hackenmiller B, Boeve B, Albers C, Kreuger C, Jones D, Knopman D, Botha H, Magnuson J, Graff-Radford J, Crawley K, Schumacher M, McKinzie S, Smith S, Helland T, Lowe V, Ramanan V, Pavlik V, Faircloth J, Bishop J, Nath J, Chaudhary M, Kataki M, Yu M, Pacini N, Barker R, Brooks R, Aggarwal R, Honig L, Stern Y, Mintz A, Cordona J, Hernandez M, Long J, Arnold A, Groves A, Middleton A, Vogler B, McCurry C, Mayo C, Raji C, Amtashar F, Klemp H, Ruszkiewicz J, Kusuran J, Stewart J, Horenkamp J, Greeson J, Wever K, Vo K, Larkin K, Rao L, Schoolcraft L, Gallagher L, Paczynski M, McMillan M, Holt M, Gagliano N, Henson R, LaBarge R, Swarm R, Munie S, Cepeda S, Winterton S, Hegedus S, Wilson T, Harte T, Bonacorsi Z, Geldmacher D, Watkins A, Barger B, Smelser B, Bates C, Stover C, McKinley E, Ikner G, Hendrix H, Cooper H, Mahaffey J, Robbins L, Ashley L, Natelson-Love M, Carter, Solomon V, Grossman H, Groome A, Ardolino A, Kaplan A, Sheppard F, Burgos-Rivera G, Garcia-Camilo G, Lim J, Neugroschl J, Jackson K, Evans K, Soleimani L, Sano M, Ghesani N, Binder S, Apuango X, Sood A, Troutman A, Blanchard K, Richards A, Nelson G, Hendrickson K, Yurko E, Plenge J, Rufo V, Shah R, Duara R, Lynch B, Chirinos C, Dittrich C, Campbell D, Mejia D, Perez G, Colvee H, Gonzalez J, Gondrez J, Knaack J, Acevedo M, Cereijo M, Greig-Custo M, Villar M, Wishnia M, Detling S, Barker W, Albert M, Moghekar A, Rodzon B, Demsky C, Pontone G, Pekar J, Farrington L, Pomper M, Johnson N, Alo T, Sadowski M, Ulysse A, Masurkar A, Marti B, Mossa D, Geesey E, Petrocca E, Schulze E, Wong J, Boonsiri J, Kenowsky S, Martinez T, Briglall V, Doraiswamy P, Nwosu A, Adhikari A, Hellegers C, Petrella J, James O, Wong T, Hawk T, Vaishnavi S, McCoubrey H, Nasrallah I, Rovere R, Maneval J, Robinson E, Rivera F, Uffelman J, Combs M, O’Donnell P, Manning S, King R, Nieto A, Glueck A, Mandal A, Swain A, Gamble B, Meacham B, Forenback D, Ross D, Cheatham E, Hartman E, Cornell G, Harp J, Ashe L, Goins L, Watts L, Yazell M, Mandal P, Buckler R, Vincent S, Rudd T, Lopez O, Malia A, Chiado C, Zik C, Ruszkiewicz J, Savage K, Fenice L, Oakley M, Tacey P, Berman S, Bowser S, Hegedus S, Saganis X, Porsteinsson A, Mathewson A, Widman A, Holvey B, Clark E, Morales E, Young I, Ruszkiewicz J, Hopkins K, Martin K, Kowalski N, Hunt R, Calzavara R, Kurvach R, D’Ambrosio S, Thai G, Vides B, Lieb B, McAdams-Ortiz C, Toso C, Mares I, Moorlach K, Liu L, Corona M, Nguyen M, Tallakson M, McDonnell M, Rangel M, Basheer N, Place P, Romero R, Tam S, Nguyen T, Thomas A, Frolov A, Khera A, Browning A, Kelley B, Dawson C, Mathews D, Most E, Phillips E, Nguyen L, Nunez M, Miller M, Jones M, Martinez N, Logan R, McColl R, Pham S, Fox T, Moore T, Levey A, Brown A, Kippels A, Ellison A, Lyons C, Hales C, Parry C, Williams C, McCorkle E, Harris G, Rose H, Jooma I, Al-Amin J, Lah J, Webster J, Swiniarski J, Chapman L, Donnelly L, Mariotti L, Locke M, Vaughn P, Penn R, Carpentier S, Yeboah S, Basadre S, Malakauskas S, Lyron S, Villinger T, Burney T, Burns J, Abusalim A, Dahlgren A, Montero A, Arthur A, Dooly H, Kreszyn K, Berner K, Gillen L, Scanlan M, Madison M, Mathis N, Switzer P, Townley R, Fikru S, Sullivan S, Wright E, Beigi M, Daley A, Ko A, Luong B, Nyborg G, Morales J, Durbin K, Garcia L, Parand L, Macias L, Monserratt L, Farchi M, Wu P, Hernandez R, Rodriguez T, Clinic M, Graff-Radford N, Marolt A, Thomas A, Aloszka D, Moncayo E, Westerhold E, Day G, Chrestensen K, Imhansiemhonehi M, McKinzie S, Stephens S, Grant S, Brosch J, Perkins A, Saunders A, Kovac D, Polson H, Mwaura I, Mejia K, Britt K, King K, Nichols K, Lawrence K, Rankin L, Farlow M, Wiesenauer P, Bryant R, Herring S, Lynch S, Wilson S, Day T, Korst W, van Dyck C, Mecca A, Miller A, Brennan A, Khan A, Ruan A, Gunnoud C, Mendonca C, Raynes-Goldfinger D, Salardini E, Hidalgo E, Cooper E, Singh E, Murphy E, May J, Stanhope J, Lam J, Waszak J, Nelsen K, Sacaza K, Hasbani M, Donahue M, Chen M, Barcelos N, Eigenberger P, Bonomi R, O’Dell R, Jefferson S, Khasnavis S, Smilowitz S, DeStefano S, Good S, Camarro T, Clayton V, Cavrel Y, Lu Y, Chertkow H, Bergman H, Hosein C, Black S, Kapadia A, Bhan A, Lam B, Scott C, Gabriel G, Bray J, Zotovic L, Gutierrez M, Masellis M, Farshadi M, Gui M, Mitchell M, Taylor R, Endre R, Taghi-Zada Z, Hsiung R, English C, Kim E, Yau E, Tong H, Barlow L, Jennings L, Assaly M, Nunes P, Marian T, Kertesz A, Rogers J, Trost D, Wint D, Bernick C, Munic D, Grant I, Korkoyah A, Raja A, Lapins A, Ryan C, Pejic J, Basham K, Lukose L, Haddad L, Quinlan L, Houghtaling N, Sadowsky C, Martinez W, Villena T, Reynolds B, Forero A, Ward C, Brennan E, Figueroa E, Esposito G, Mallory J, Johnson K, Turner K, Seidenberg K, McCann K, Bassett M, Chadwick M, Turner R, Bean R, Sharma S, Marshall G, Haviari A, Pietras A, Wallace B, Munro C, Rivera-Delpin G, Hustead H, Levesque I, Ramirez J, Nolan K, Glennon K, Palou M, Erkkinen M, DaSilva N, Friedman P, Silver R, Salazar R, Polleys R, McGinnis S, Gale S, Hall T, Luu T, Chao S, Lin E, Coleman J, Epperson K, Vasanawala M, Atri A, Rangel A, Evans B, Monarrez C, Cline C, Liebsack C, Bandy D, Goldfarb D, Intorcia D, Olgin J, Clark K, King K, York K, Reade M, Callan M, Glass M, Johnson M, Gutierrez M, Goddard M, Trncic N, Choudhury P, Reyes P, Lowery S, Hall S, Olgin S, de Santiago S, Alosco M, Ton A, Jimenez A, Ellison A, Tran A, Anderson B, Carter D, Veronelli D, Lenio S, Steinberg E, Mez J, Weller J, Johns J, Mez J, Harkins J, Puleio A, Hoti I, Mwicigi J, Puleio A, Alosco M, Schultz O, Lauture M, Steinberg E, Denis R, Killiany R, Singh S, Lenio S, Qiu W, Devis Y, Obisesan T, Stone A, Ordor D, Udodong I, Okonkwo I, Khan J, Turner J, Hughes K, Kadiri O, Duffy C, Moss A, Stapleton K, Toth M, Sanders M, Ayres M, Hamski M, Fatica P, Ogrocki P, Ash S, Pot S, Chen D, Soto A, Tanase C, Bissig D, Vanya H, Russell H, Patel H, Zhang H, Wallace K, Ayers K, Gallegos M, Forloines M, Sinn M, Kahulugan Q, Isip R, Calderon S, Hamm T, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Perrin A, Tariot P, Fleisher A, Reeder S, Capote H, Emborsky A, Mattle A, Ajtai B, Wagner B, Myers B, Slazyk D, Fragale D, Fransen E, Macnamara H, Falletta J, Hirtreiter J, Mechtler L, King M, Asbach M, Rainka M, Zawislak R, Wisniewski S, O’Malley S, Jimenez-Knight T, Peehler T, Aladeen T, Bates V, Wenner V, Elmalik W, Scharre D, Ramamurthy A, Bouchachi S, Kataki M, Tarawneh R, Kelley B, Celmins D, Leader A, Figueroa C, Bauerle H, Patterson K, Reposa M, Presto S, Ahmed T, Stewart W, Hosp H, Pearlson G, Blank K, Anderson K, Santulli R, Schwartz E, Williamson J, Jessup A, Williams A, Duncan C, O’Connell A, Gagnon K, Zamora E, Bateman J, Crawford F, Thompson D, Walker E, Rowell J, White M, Ledford P, Bohlman S, Henkle S, Bottoms J, Moretz L, Hoover B, Shannon M, Rogers S, Baker W, Harrison W, Wu C, DeMarco A, Stipanovich A, Arcuri D, Clark J, Davis J, Doyon K, Amoyaw M, Acosta M, Bailey R, Warren S, Fogerty T, Sanborn V, Riddle M, Salloway S, Malloy P, Correia S, Windon C, Blackburn M, Rosen H, Miller B, Smith A, Mba I, Echevarria J, Janavs J, Roglaski E, Yong M, Devine R, Okhravi H, Rivera E, Kalowsky T, Smith C, Rosario C, Masdeu J, Le R, Gurung M, Sabbagh M, Garcia A, Slaughter M, Elayan N, Acothley S, Pomara N, Hernando R, Pomara V, Reichert C, Brawman-Mintzer O, Acree A, Williams A, Long C, Long R, Newhouse P, Hill S, Boegel A, Seshadri S, Saklad A, Jones F, Hu W, Sotelo V, Rojas Y, Mintzer J, Longmire C, Spicer K. Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2411492122. PMID: 39977323, PMCID: PMC11873959, DOI: 10.1073/pnas.2411492122.Peer-Reviewed Original ResearchConceptsFully connected layerLearning-based methodsMachine learning-based methodsSignal-of-interestImage dataSiamese architectureLongitudinal imaging dataBaseline methodsFeature pairsTime-varying signalsImage differencesSquare errorPatient monitoringRoot mean square errorTraditional methodsOrder accuracyTime differenceCustom pipelineIrrelevant changesTemporal orderEmpirical resultsImagesArchitectureIndividual-level changeMachineGovernance Considerations for Point-of-Care Ultrasound: a HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM
Ma I, Francavilla M, Nomura J, Kielski A, Fernandez F, Piro K, Liu R, Valenzuela J, Toland M, Koehler J, Cohen G, Eid M, Choi W, Nolan J, Ferre R, McBee M, Kummer T, Lanspa M, Brown J, DeStigter K, Desyatnikova S, Bottemiller A. Governance Considerations for Point-of-Care Ultrasound: a HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM. Journal Of Imaging Informatics In Medicine 2025, 1-15. DOI: 10.1007/s10278-024-01365-7.Peer-Reviewed Original ResearchEnterprise imagePoint-of-care ultrasoundWorkflow solutionTechnology governanceWorkflow needsInformation governancePatient care outcomesPoint-of-careUnique workflowCare outcomesClinical governancePoint-of-care ultrasound imagingHealthcare settingsHealthcare systemOperational efficiencyImagesImplementationInformationHealthcareEducational requirementsGovernance committeeClinical informationInstitutional policiesWorkflowGovernance Considerations for Point-of-Care Ultrasound: a HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM.
Ma I, Francavilla M, Nomura J, Kielski A, Fernandez F, Piro K, Liu R, Valenzuela J, Toland M, Koehler J, Cohen G, Eid M, Choi W, Nolan J, Ferre R, McBee M, Kummer T, Lanspa M, Brown J, DeStigter K, Desyatnikova S, Bottemiller A. Governance Considerations for Point-of-Care Ultrasound: a HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM. Journal Of Imaging Informatics In Medicine 2025 PMID: 39753828, DOI: 10.1007/s10278-024-01365-7.Peer-Reviewed Original ResearchEnterprise imagePoint-of-care ultrasoundWorkflow solutionTechnology governanceWorkflow needsInformation governancePatient care outcomesPoint-of-careUnique workflowCare outcomesClinical governancePoint-of-care ultrasound imagingHealthcare settingsHealthcare systemOperational efficiencyImagesImplementationInformationHealthcareEducational requirementsGovernance committeeClinical informationInstitutional policiesWorkflowDeep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Guo M, Wu Y, Hobson C, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Colón-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Nature Communications 2025, 16: 313. PMID: 39747824, PMCID: PMC11697233, DOI: 10.1038/s41467-024-55267-x.Peer-Reviewed Original ResearchConceptsAdaptive optics techniquesMulti-photonDeep learning-based strategyAberration compensationLearning-based strategyTrained neural networkImprove image qualityOptical aberrationsNeural networkImage quantitationOptical techniquesDiverse datasetsSuper-resolution microscopyLight sheetRestore dataImage qualityImage signalNetworkImage inspectionImage acquisitionImage stacksOpticsResolutionImagesFluorescence microscopyLeveraging synthetic imagery and YOLOv8 for a novel colorimetric approach to paper-based point-of-care male fertility testing
Özarslan O, Tokyay B, Soylemez C, Birtek M, Uygun Z, Keles İ, Aydogan Mathyk B, Halicigil C, Tasoglu S. Leveraging synthetic imagery and YOLOv8 for a novel colorimetric approach to paper-based point-of-care male fertility testing. Sensors & Diagnostics 2025 DOI: 10.1039/d4sd00348a.Peer-Reviewed Original ResearchSynthetic imageryPaper-based systemObject detection algorithmDetection algorithmFertility monitorSynthetic dataDisease diagnosisPoint-of-careMale fertility testingSmartphone imagesCost-effective solutionSemen samplesSystem's abilityTraditional methodsFace stigmaLight conditionsPaper-based assayPOC diagnosticsFertility testsInfectious disease diagnosisBiochemical analysisImagesImageryPaper-based testSmartphone
2024
Bayesian thresholded modeling for integrating brain node and network predictors
Sun Z, Xu W, Li T, Kang J, Alanis-Lobato G, Zhao Y. Bayesian thresholded modeling for integrating brain node and network predictors. Biostatistics 2024, 26: kxae048. PMID: 39780514, PMCID: PMC11823287, DOI: 10.1093/biostatistics/kxae048.Peer-Reviewed Original ResearchConceptsPrediction mechanismNetwork-level metricsExtensive simulationsNetwork predictorPrior modelsSub-networksVector-variantPosterior inferenceNodesSignal patternsPredictable componentBrain nodesSpatial contiguityBayesian regression modelsImagesHierarchyLiterature gapNetworkMetricsCommunicationAlternative approachOut-of-sample predictionsInferenceModelIdentification 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 modelUnsupervisedVideoClustersCloudLeveraging CNNs for Automated Dermatological Diagnosis: Advancements in Skin Disease Prediction
Yenkikar A, Khandave A, Kukreja G, Chorghade A, Patil A, Dhanashri P. Leveraging CNNs for Automated Dermatological Diagnosis: Advancements in Skin Disease Prediction. International Journal For Research In Applied Science And Engineering Technology 2024, 12: 1511-1524. DOI: 10.22214/ijraset.2024.65440.Peer-Reviewed Original ResearchSkin disease predictionCNN modelDisease predictionAnalysis of medical imagesMassive data amountsApplication of CNNClasses of dataData pre-processingInput imageLeverage CNNCNN algorithmData amountCNNClassification processMedical imagesPre-processingAccuracy rateImage featuresResearch paperProblem statementRight timeImagesAccurate wayNoise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai Y, Miao T, Xia M, Liu Y, Armstrong I, Wang G, Carson R, Sinusas A, Liu C. Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. Medical Image Analysis 2024, 100: 103391. PMID: 39579623, PMCID: PMC11647511, DOI: 10.1016/j.media.2024.103391.Peer-Reviewed Original ResearchImage denoisingPositron range correctionDynamic framesSelf-supervised methodsSuperior visual qualityLow signal-to-noise ratioCardiac PET imagingDenoising methodSignal-to-noise ratioSelf-supervisionVisual qualityHigh-energy positronsRange correctionsDenoisingNoise levelImage spatial resolutionImage qualityDefect contrastPET imagingImage quantificationRadioactive isotopesPatient scansQuantitative accuracyImagesFrameDose-aware Diffusion Model for 3D Low-count Cardiac SPECT Image Denoising with Projection-domain Consistency
Xie H, Gan W, Chen X, Zhou B, Liu Q, Xia M, Guo X, Liu Y, An H, Kamilov U, Wang G, Sinusas A, Liu C. Dose-aware Diffusion Model for 3D Low-count Cardiac SPECT Image Denoising with Projection-domain Consistency. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10655170.Peer-Reviewed Original ResearchImage denoisingImage denoising performanceDeep learning techniquesNoise-levelDenoising performanceDenoising resultsNeural networkLearning techniquesSPECT imagesLow count levelsSPECT scansDenoisingSampling stepIterative reconstructionNoise amplitudeImagesInjected dosePatient studiesDiffusion modelRadiation exposureCardiology studiesSPECTNetworkStochastic natureMLEMAnatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising
Xia M, Xie H, Liu Q, Guo L, Ouyang J, Bayerlein R, Spencer B, Badawi R, Li Q, Fakhri G, Liu C. Anatomically and Metabolically Informed Deep Learning Low-Count PET Image Denoising. 2024, 00: 1-2. DOI: 10.1109/nss/mic/rtsd57108.2024.10657099.Peer-Reviewed Original ResearchDeep learningOver-smoothed imagesDL training processesHigh-count imagesImage denoisingDenoised imageLow-count dataSemantic informationSemantic classesSegmentation guidanceTraining processPET/CT systemHistogram distributionImage qualitySegmentation toolPositron emission tomographyImagesDenoisingDatasetHistogramPriorsRadiation exposurePET Image Denoising Based on 3D Denoising Diffusion Probabilistic Model: Evaluations on Total-Body Datasets
Yu B, Ozdemir S, Dong Y, Shao W, Shi K, Gong K. PET Image Denoising Based on 3D Denoising Diffusion Probabilistic Model: Evaluations on Total-Body Datasets. Lecture Notes In Computer Science 2024, 15007: 541-550. DOI: 10.1007/978-3-031-72104-5_52.Peer-Reviewed Original ResearchDenoising diffusion probabilistic modelPET image denoisingDiffusion probabilistic modelImage denoisingFDG-PET datasetComputer-vision tasksProbabilistic modelLow-dose imagesConvolutional networkDenoisingEdge contourGenerative modelScoring functionDatasetPET datasetsPhysical degrading factorsExperimental resultsPositron emission tomographyNetworkPhoton countingTest dataImagesHigh confidenceUNetDegradation factorsA Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, PMCID: PMC11609020, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imagingLow-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging
Samardzija A, Selvaganesan K, Zhang H, Sun H, Sun C, Ha Y, Galiana G, Constable R. Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging. Annual Review Of Biomedical Engineering 2024, 26: 67-91. PMID: 38211326, DOI: 10.1146/annurev-bioeng-110122-022903.Peer-Reviewed Original ResearchComputational reconstruction of mental representations using human behavior
Caplette L, Turk-Browne N. Computational reconstruction of mental representations using human behavior. Nature Communications 2024, 15: 4183. PMID: 38760341, PMCID: PMC11101448, DOI: 10.1038/s41467-024-48114-6.Peer-Reviewed Original ResearchConceptsMental representationsGoal of cognitive scienceVisual featuresNeural networkMultiple visual conceptsDeep neural networksConceptual representationCognitive scienceVisual conceptsSemantic spaceSemantic featuresHuman behaviorParticipantsNetworkRepresentationStimuliBehaviorFeaturesImagesComputer reconstructionTaskImages with harder-to-reconstruct visual representations leave stronger memory traces
Lin Q, Li Z, Lafferty J, Yildirim I. Images with harder-to-reconstruct visual representations leave stronger memory traces. Nature Human Behaviour 2024, 8: 1309-1320. PMID: 38740989, DOI: 10.1038/s41562-024-01870-3.Peer-Reviewed Original ResearchReconstruction errorFeature embeddingScene imagesAdaptive modulationLevels of processing theoryReconstruction residualsStronger memory tracesPerception interfaceInterface perceptionMemory accuracyVisual representationInfluence memoryPerceptual processingMemory durabilityMemory tracesResponse latencyMemoryImagesErrorEmbeddingDatasetArchitectureRetrievalIntentional selectionReconstructionEfficient Standardization of Clinical T2-Weighted Images: Phase-Conjugacy e-CAMP with Projected Gradient Descent
Zhang H, Elsaid N, Sun H, Tagare H, Galiana G. Efficient Standardization of Clinical T2-Weighted Images: Phase-Conjugacy e-CAMP with Projected Gradient Descent. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/2733.Peer-Reviewed Original ResearchProjected Gradient DescentGradient descentMachine learningLarge-scale machine learningMassive data sourcesMap reconstructionParameter tuningRobust implementationParameter choicesEfficient enforcementData sourcesModel constraintsVirtual conjugate coilLearningImagesRoutine clinical imagingVirtualClinical imagesAlgorithmDatasetClinical scansParameter valuesMachineDescentQuantitative imagingA Unified Approach for Synthesizing Multimodal Brain MR Images via Gated Hybrid Fusion
Cho J, Liu X, Xing F, Ouyang J, El Fakhri G, Park J, Woo J. A Unified Approach for Synthesizing Multimodal Brain MR Images via Gated Hybrid Fusion. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/2242.Peer-Reviewed Original ResearchMulti-purposed diagnostic system for ovarian endometrioma using CNN and transformer networks in ultrasound
Li Y, Zhao B, Wen L, Huang R, Ni D. Multi-purposed diagnostic system for ovarian endometrioma using CNN and transformer networks in ultrasound. Biomedical Signal Processing And Control 2024, 91: 105923. DOI: 10.1016/j.bspc.2023.105923.Peer-Reviewed Original ResearchFirst deep learning methodDeep learning methodsAccuracy of classificationDeep learningSegmentation branchTransformer networkClassification branchPopular networksAttention mechanismDice scoreCyst segmentationLearning methodsClassification accuracyComparable performanceExperience of doctorsReader studyDiagnostic systemNetworkClassificationSpecific informationAccuracyImagesCNNCyst regionExtensive dataset
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