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Medical Image Analysis|July 23, 2024
DMSPS: Dynamically mixed soft pseudo-label supervision for scribble-supervised medical image segmentationMeng Han, Xiangde Luo, Xiangjiang Xie, et al.
IEEE Journal of Biomedical and Health Informatics|June 10, 2022
HMRNet: High and Multi-Resolution Network With Bidirectional Feature Calibration for Brain Structure Segmentation in RadiotherapyHao Fu, Guotai Wang, Wenhui Lei, et al.
International Journal of Radiation Biology|November 17, 2020
FOXG1 mediates the radiosensitivity of glioma cells through regulation of autophagyNing Xiao, Churong Li, Wenjun Liao, et al.
Advances in Therapy|September 14, 2018
Diagnostic Accuracy of Bronchodilator Response for Asthma in a Population of South ChinaShichuan Zhang, Chengping Hu, Rongli Lu, et al.
Medical Physics|February 10, 2023
Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networksQianfei Zhao, Guotai Wang, Wenhui Lei, et al.
Scientific Reports|May 23, 2026
Development and validation of a self-management ability scale for patients with head and neck cancer undergoing radiotherapy and chemotherapy (SMA-HNC)Jianxia Lyu, Hao Zhang, Li Yin, et al.
Medical Physics|October 5, 2021
Automatic segmentation of organs-at-risks of nasopharynx cancer and lung cancer by cross-layer attention fusion network with TELD-LossZuhao Liu, Chao Sun, Huan Wang, et al.
Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology|May 2, 2022
Level Ib CTV delineation in nasopharyngeal carcinoma based on lymph node distribution and topographic anatomyYue Zhao, Xiongfei Liao, Yiling Wang, et al.
Medical Image Analysis|November 30, 2025
PL-Seg: Partially labeled abdominal organ segmentation via classwise orthogonal contrastive learning and progressive self-distillationHe Li, Xiangde Luo, Jia Fu, et al.
International Journal of Radiation Oncology, Biology, Physics|April 10, 2026
Is deep learning ready for abdominal organ-at-risk segmentation in the foundation model era: A comprehensive study of challenging clinical casesJia Fu, He Li, Zihao Luo, et al.
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