2024
POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Zhou B, Hou J, Chen T, Zhou Y, Chen X, Xie H, Liu Q, Guo X, Xia M, Tsai Y, Panin V, Toyonaga T, Duncan J, Liu C. POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10658051.Peer-Reviewed Original ResearchPET attenuation correctionLow-dose PETAttenuation correctionU-mapAttenuation mapElevated radiation doseRadiation doseEfficient feature extractionRadiation exposurePET imagingFinely detailed featuresBaseline methodsMitigate radiation exposureFeature extractionCorrectionMap generationGeneration machines
2023
Meta-information-Aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT
Zhou B, Xia Y, Yao J, Lu L, Zhou J, Liu C, Duncan J, Zhang L. Meta-information-Aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT. Lecture Notes In Computer Science 2023, 13939: 119-131. DOI: 10.1007/978-3-031-34048-2_10.Peer-Reviewed Original ResearchFull taxonomyInitial feature extractionGlobal contextual informationPatient-level classificationSemantic segmentationFeature extractionClass labelsTransformer blocksManual annotationContextual informationSegmentation pathSegmentationAccurate classificationPrevious baselineFeasibility of classificationCT datasetsMulti-phase CTPatient informationWork focusAccurate detectionClassificationModel performanceDisease diagnosisPrevious workCNN