MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization. - Ke Wang; Zanting Ye; Xiang Xie; Haidong Cui; Tao Chen; Banteng Liu (CoRR, 2023)

SSL-DG: Rethinking and Fusing Semi-supervised Learning and Domain Generalization in Medical Image Segmentation. - Zanting Ye (CoRR, 2023)

MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization. - Ke Wang 048; Zanting Ye; Xiang Xie; Haidong Cui; Tao Chen; Banteng Liu (Knowl. Based Syst., 2024)

FSDA-DG: Improving cross-domain generalizability of medical image segmentation with few source domain annotations. - Zanting Ye; Ke Wang 048; Wenbing Lv; Qianjin Feng; Lijun Lu (Medical Image Anal., 2025)

Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning. - Zanting Ye; Xiaolong Niu; Xuanbin Wu; Wantong Lu; Lijun Lu (CoRR, 2025)

Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation. - Zanting Ye; Xiaolong Niu; Xuanbin Wu; Wenxiang Yi; Yuan Chang; Lijun Lu (CoRR, 2025)

MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising. - Xiaolong Niu; Zanting Ye; Xu Han; Yanchao Huang; Hao Sun; Hubing Wu; Lijun Lu (CoRR, 2025)

Yolo-HLSAM: Adapting foundation segment anything model for semi-automatic detection and segmentation of breast cancer microcalcification clusters. - Banteng Liu; Hongguang Chen; Tianyi Zhu; Zanting Ye; Haidong Cui; Ke Wang 048 (Biomed. Signal Process. Control., 2026)