Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms. - Xinyu Xiong; Churan Wang; Wenxue Li 003; Guanbin Li (MLMI@MICCAI (1), 2023)

LACINet: A Lesion-Aware Contextual Interaction Network for Polyp Segmentation. - Wenxue Li 003; Wei Lu 026; Jinghui Chu; Fugui Fan (IEEE Trans. Instrum. Meas., 2023)

Confidence-guided mask learning for semi-supervised medical image segmentation. - Wenxue Li 003; Wei Lu 026; Jinghui Chu; Qi Tian; Fugui Fan (Comput. Biol. Medicine, 2023)

Semi- and Weakly-Supervised Learning for Mammogram Mass Segmentation with Limited Annotations. - Xinyu Xiong; Churan Wang; Wenxue Li 003; Guanbin Li (ISBI, 2024)

SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation. - Xinyu Xiong; Zihuang Wu; Shuangyi Tan; Wenxue Li 003; Feilong Tang; Ying Chen; Siying Li; Jie Ma; Guanbin Li (CoRR, 2024)

HybridVPS: Hybrid-Supervised Video Polyp Segmentation Under Low-Cost Labels. - Wenxue Li 003; Xinyu Xiong; Siying Li; Fugui Fan (IEEE Signal Process. Lett., 2024)

Free Meal: Boosting Semi-Supervised Polyp Segmentation by Harvesting Negative Samples. - Xinyu Xiong; Wenxue Li 003; Jie Ma; Duojun Huang; Siying Li (IEEE Signal Process. Lett., 2025)