grant
Toward efficient performance for deep learning on medical imaging
Organization UNIVERSITY OF CALIFORNIA, SAN FRANCISCOLocation SAN FRANCISCO, UNITED STATESPosted 1 Apr 2020Deadline 31 Mar 2030
NIHUS FederalResearch GrantFY20261st trimester2nd trimesterAccelerationAccuracy of DiagnosisAwardBenchmarkingBest Practice AnalysisBiometricsBiometryBiophysicsBiostatisticsBirthBirth DefectsCardiacCardiac MalformationCardiovascularCardiovascular Body SystemCardiovascular Organ SystemCardiovascular systemClassificationClinicalCollectionCommunitiesComputer softwareCongenital AbnormalityCongenital Anatomical AbnormalityCongenital DefectsCongenital DeformityCongenital MalformationDataData ScienceData SetDetectionDiagnosisDiagnosticEarly DiagnosisEarly Placental PhaseEarly treatmentEchographyEchotomographyEnvironmentEvaluationFetal HeartFetal TherapiesFetusFirst Pregnancy TrimesterFirst TrimesterFoundationsGestationGoalsGuidelinesHeart MalformationHeart VascularHumanImageImage AnalysesImage AnalysisInformaticsInternationalKnowledgeLabelLearningLeftLifeLiteratureMachine LearningManualsMedical ImagingMedical UltrasoundMedical centerMedicineMethodsMidtrimesterModelingModern ManMorbidityNatureOrphan DiseaseOutcomePaperParturitionPatientsPerformancePhysicsPopulationPregnancyPregnant WomenQuality ControlRare DiseasesRare DisorderRecommendationReproducibilityResearchSecond Pregnancy TrimesterSecond TrimesterSecureSoftwareSurvey InstrumentSurveysSystematicsTechniquesTestingTimeTrainingUltrasonic ImagingUltrasonogramUltrasonographyUltrasound DiagnosisUltrasound Medical ImagingUltrasound TestUpdateValidationVariantVariationWorkabnormal heart developmentbenchmarkbiophysical foundationbiophysical principlesbiophysical sciencescardiac disorder diagnosiscirculatory systemclinical centerclinical decision-makingclinical imagingclinical relevanceclinical translationclinically relevantclinically translatablecloud basedco-morbidco-morbiditycomorbiditycongenital cardiac abnormalitycongenital cardiac anomaliescongenital cardiac diseasecongenital cardiac disordercongenital cardiac malformationcongenital heart abnormalitycongenital heart anomalycongenital heart diseasecongenital heart disordercongenital heart malformationcostdata preservationdeep learningdeep learning based modeldeep learning methoddeep learning modeldeep learning strategydesigndesigningdevelop softwaredeveloping computer softwarediagnosed with cardiac diseasediagnosed with heart diseasediagnostic accuracydiagnostic ultrasoundearly detectionearly therapyexpectant motherexpectant womenexpecting motherexpecting womenfeature extractionfetalfetal diagnosisfetus therapyhealth care settingsheart disease diagnosisheart disorder diagnosisimage evaluationimage interpretationimage processingimagingimaging approachimaging based approachimprovedin utero therapyindividuals who are pregnantinsightmachine based learningmachine learning based methodmachine learning methodmachine learning methodologiesmortalitymulti-task learningmultidisciplinarymultitask learningneural networknovelopen sourceorphan disorderpeople who are pregnantpregnant femalespregnant motherspregnant peoplepregnant populationsprenatalprenatal therapyprogramsprototyperepairrepairedscreeningscreeningsself supervisedself supervised learningself supervisionsoftware developmentsonogramsonographysound measurementtheoriesthose who are pregnanttoolultrasoundultrasound imagingultrasound scanningunbornvalidationswomen who are pregnant
Description preview
Project Summary / Abstract
Objective — The goal of this renewal is to continue developing and optimizing novel deep learning (DL) and
related methods to improve diagnosis and clinical decision-making for congenital heart disease (CHD). Nota-
bly, this work includes a clinical translational evaluation of these methods in a population-wide imaging…
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