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2020 – today
- 2026
[j5]Yao Ai, Qiao Zheng, Xiaoyang Zhu, Jianping Wu, Yapeng Liu, Yiyang Wu, Wenjing Wang, Kangwei Zhu, Ziqing Xiang, Xiaojian Yan, Yan Hu, Xiance Jin:
Explainable radiomic-deep learning model with SHAP for predicting lymph-vascular space invasion in operable cervical cancer: a multicenter study. Biomed. Signal Process. Control. 113: 109236 (2026)- 2025
- [j4]Feixiang Zhao
, Mingzhe Liu, Mingrong Xiang, Dongfen Li, Xin Jiang, Xiance Jin, Cai Lin, Ruili Wang:
Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies. J. Imaging Inform. Medicine 38(2): 902-930 (2025) - 2024
- [j3]Wanyu Su, Dezhi Cheng, Weihua Ni, Yao Ai, Xianwen Yu, Ninghang Tan, Jianping Wu, Wen Fu, Chenyu Li, Congying Xie, Meixiao Shen, Xiance Jin:
Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy. Comput. Methods Programs Biomed. 254: 108295 (2024) - 2022
- [j2]Juebin Jin, Haiyan Zhu, Yingyan Teng, Yao Ai, Congying Xie, Xiance Jin:
The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer. J. Digit. Imaging 35(4): 983-992 (2022) - [j1]Bing Yu, Yanyan Li, Xiangle Yu, Yao Ai, Juebin Jin, Ji Zhang, YuHua Zhang, Hui Zhu, Congying Xie, Meixiao Shen, Yan Yang, Xiance Jin:
Differentiate Thyroid Follicular Adenoma from Carcinoma with Combined Ultrasound Radiomics Features and Clinical Ultrasound Features. J. Digit. Imaging 35(5): 1362-1372 (2022)
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