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 distribution
2022
Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation
Liu X, Xing F, El Fakhri G, Woo J. Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation. Medical Image Analysis 2022, 83: 102641. PMID: 36265264, PMCID: PMC10016738, DOI: 10.1016/j.media.2022.102641.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationUnsupervised domain adaptation methodsSource domain dataBN statisticsTarget domainLabeled source domain dataDomain dataLabeled source domainSelf-training strategyPatient data privacyHeterogeneous target domainBrain tumor segmentationPseudo-labelsDomain adaptationUnsupervised adaptationData privacySegmentation taskSource domainImage segmentationVital protocolAdaptation frameworkDecay strategyBoost performanceModel adaptationTumor segmentation
2008
Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method
Lo H, Chang C, Chiang T, Hsiao C, Huang A, Kuo T, Lai W, Yang M, Yeh J, Yen C, Lin S. Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method. ACM SIGKDD Explorations Newsletter 2008, 10: 43-46. DOI: 10.1145/1540276.1540290.Peer-Reviewed Original Research
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