2024
Deep-RPD-Net: A 3D Deep Network for Detection of Reticular Pseudodrusen on Optical Coherence Tomography Scans
Elsawy A, Keenan T, Thavikulwat A, Lu A, Bellur S, Mukherjee S, Agron E, Chen Q, Chew E, Lu Z. Deep-RPD-Net: A 3D Deep Network for Detection of Reticular Pseudodrusen on Optical Coherence Tomography Scans. Ophthalmology Science 2024, 100655. DOI: 10.1016/j.xops.2024.100655.Peer-Reviewed Original ResearchSemi-supervised learningReticular pseudodrusenOCT scansRetina specialistsOptical coherence tomographyArea under ROC curveSpectral-domain optical coherence tomographyBaseline modelOptical coherence tomography scansAge-Related Macular Degeneration StudyDetect reticular pseudodrusenFundus autofluorescence imagingDeep learning networkDeep networksBaseline methodsPretrained modelsModel decision-makingReading centerLearning networkHigh-performance metricsOCT studiesTomography scanAREDS2En faceCoherence tomography
2022
Assigning species information to corresponding genes by a sequence labeling framework
Luo L, Wei C, Lai P, Chen Q, Islamaj R, Lu Z. Assigning species information to corresponding genes by a sequence labeling framework. Database 2022, 2022: baac090. PMID: 36227127, PMCID: PMC9558450, DOI: 10.1093/database/baac090.Peer-Reviewed Original ResearchConceptsNovel deep learning-based frameworkDeep learning-based frameworkLearning-based frameworkText mining algorithmsSequence labeling taskGene normalization taskSequence labeling frameworkBinary classification frameworkSource codeBaseline methodsNormalization taskClassification frameworkLabeling taskLabeling frameworkAutomatic assignmentHigh-performance methodHeuristic rulesGene mentionsBenchmarking resultsDatabase URLDatabase recordsAssignment task