2024
Augmenting biomedical named entity recognition with general-domain resources
Yin Y, Kim H, Xiao X, Wei C, Kang J, Lu Z, Xu H, Fang M, Chen Q. Augmenting biomedical named entity recognition with general-domain resources. Journal Of Biomedical Informatics 2024, 159: 104731. PMID: 39368529, DOI: 10.1016/j.jbi.2024.104731.Peer-Reviewed Original ResearchBioNER datasetsMulti-task learningNER datasetsEntity typesBiomedical datasetsBaseline modelGeneral domain datasetsBiomedical language modelNeural network-basedYield performance improvementsBioNER modelsEntity recognitionBiomedical corporaHuman annotatorsLabel ambiguityLanguage modelTransfer learningF1 scoreBioNERHuman effortNetwork-basedBiomedical resourcesPerformance improvementDatasetSuperior performanceDetecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning
Lee D, Jeon H, Son S, Park C, An J, Kim S, Han J. Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning. 2024, 4954-4970. DOI: 10.18653/v1/2024.naacl-long.278.Peer-Reviewed Original Research
2023
Multi-task Learning for Hierarchically-Structured Images: Study on Echocardiogram View Classification
Charton J, Ren H, Kim S, Gonzalez C, Khambhati J, Cheng J, DeFrancesco J, Waheed A, Marciniak S, Moura F, Cardoso R, Lima B, Picard M, Li X, Li Q. Multi-task Learning for Hierarchically-Structured Images: Study on Echocardiogram View Classification. Lecture Notes In Computer Science 2023, 14337: 185-194. DOI: 10.1007/978-3-031-44521-7_18.Peer-Reviewed Original ResearchDeep learningMulti-task learning schemeVideo classification tasksMulti-task learningImage classification scenariosResidual neural networkData labelsClassification taskLearning schemeAblation studiesClassification scenariosNeural networkTree structureMedical imagesImage-processing techniquesSuperior performanceVideo viewingMonitoring cardiovascular diseasesTraining methodsHierarchically-structuredImage acquisitionLearningHierarchical structureTaskClassificationMachine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders
Zhu T, Wang W, Chen Y, Kranzler H, Li C, Bi J. Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2023, 9: 326-336. PMID: 37696489, PMCID: PMC10976073, DOI: 10.1016/j.bpsc.2023.08.010.Peer-Reviewed Original ResearchNicotine use disorderHealthy controlsFunctional connectivity featuresUse disordersMagnetic resonance imagingNUD subjectsVisual cortexResonance imagingClinical metricsFunctional connectivityNoninvasive toolNeural phenotypesSample of individualsMulti-task learningTransdiagnostic approachUK BiobankReplication setGenetic profileMarkersReplication sampleHighest areaDisordersDepressionAUDBody of literatureBioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets
Lai P, Wei C, Luo L, Chen Q, Lu Z. BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets. Journal Of Biomedical Informatics 2023, 146: 104487. PMID: 37673376, DOI: 10.1016/j.jbi.2023.104487.Peer-Reviewed Original ResearchBiomedical relation extractionRelation extractionRE tasksNatural language processing researchData-centric approachKnowledge graph constructionMulti-task learningLanguage processing researchIndividual datasetsLiterature-based discoveryChemical-induced disease relationsDataset annotationDomain knowledgeTransfer learningTraining dataHeterogeneous datasetsArt methodsNovel frameworkGraph constructionFree textData heterogeneityLarge datasetsBiomedical conceptsProcessing researchDatasetAIONER: all-in-one scheme-based biomedical named entity recognition using deep learning
Luo L, Wei C, Lai P, Leaman R, Chen Q, Lu Z. AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning. Bioinformatics 2023, 39: btad310. PMID: 37171899, PMCID: PMC10212279, DOI: 10.1093/bioinformatics/btad310.Peer-Reviewed Original ResearchConceptsDeep learningEntity recognitionTraining dataEntity typesLabeling training dataNatural language textText mining tasksSignificant domain expertiseMulti-task learningMining tasksInformation extractionBioNER taskDomain expertiseBiomedical entitiesIndependent tasksSource codeBenchmark tasksLanguage textBiomedical textArt approachesAccurate annotationExternal dataData scarcityTaskLearning
2022
Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning
Ta K, Ahn SS, Stendahl JC, Langdon J, Sinusas AJ, Duncan JS. Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography Using Multi-task Learning. Lecture Notes In Computer Science 2022, 13131: 123-131. PMID: 35759335, PMCID: PMC9221412, DOI: 10.1007/978-3-030-93722-5_14.Peer-Reviewed Original ResearchMotion estimationMulti-task learning networkMedical image analysis literatureMulti-task learningSingle-task learningMotion estimation techniqueImage analysis literatureComputer visionDecoding branchesFeature encoderLearning frameworkLearning networkLatent featuresAccurate segmentationSimultaneous segmentationEstimate motionImage pairsTask learningRealistic motion patternsVolumetric segmentationSegmentationMotion patternsTaskUnique taskLearning
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