DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images. - Haoming Li 003; Huiyan Jiang; Siqi Li 002; Meng Wang 029; Zhiguo Wang 001; Guoxiu Lu; Jia Guo; Youchao Wang (IEEE Access, 2020)

PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation. - Meng Wang 029; Huiyan Jiang; Tianyu Shi; Zhiguo Wang 001; Jia Guo; Guoxiu Lu; Youchao Wang; Yu-Dong Yao (Comput. Biol. Medicine, 2022)

HD-RDS-UNet: Leveraging Spatial-Temporal Correlation Between the Decoder Feature Maps for Lymphoma Segmentation. - Meng Wang 029; Huiyan Jiang; Tianyu Shi; Yu-Dong Yao (IEEE J. Biomed. Health Informatics, 2022)

C2BA-UNet: A context-coordination multi-atlas boundary-aware UNet-like method for PET/CT images based tumor segmentation. - Shijie Luo; Huiyan Jiang; Meng Wang 029 (Comput. Medical Imaging Graph., 2023)

SCL-Net: Structured Collaborative Learning for PET/CT Based Tumor Segmentation. - Meng Wang 029; Huiyan Jiang; Tianyu Shi; Yu-Dong Yao (IEEE J. Biomed. Health Informatics, 2023)

Metabolic Anomaly Appearance Aware U-Net for Automatic Lymphoma Segmentation in Whole-Body PET/CT Scans. - Tianyu Shi; Huiyan Jiang; Meng Wang 029; Zhaoshuo Diao; Guoxu Zhang; Yu-Dong Yao (IEEE J. Biomed. Health Informatics, 2023)

TG-Net: Using text prompts for improved skin lesion segmentation. - Xiangfu Meng; Chunlin Yu; Zhichao Zhang; Xiaoyan Zhang 005; Meng Wang 029 (Comput. Biol. Medicine, 2024)