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Siqiong Yao

Showing results (1-10 of 15) with videos related to

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Nucleic Acids Research|October 9, 2023
tRFtarget 2.0: expanding the targetome landscape of transfer RNA-derived fragmentsNingshan Li, Siqiong Yao, Guangjun Yu, et al.
Nature Communications|March 4, 2024
Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule populationSiqiong Yao, Fang Dai, Peng Sun, et al.
IEEE Transactions on Bio-Medical Engineering|August 19, 2025
Classification of Craniomaxillofacial Free Flap: Mechanism and ModelYuhang Men, Jing Han, Siqiong Yao, et al.
Iscience|May 1, 2023
Human understandable thyroid ultrasound imaging AI report system - A bridge between AI and cliniciansSiqiong Yao, Pengcheng Shen, Tongwei Dai, et al.
Frontiers in Bioengineering and Biotechnology|July 7, 2020
Texture Synthesis Based Thyroid Nodule Detection From Medical Ultrasound Images: Interpreting and Suppressing the Adversarial Effect of In-place Manual AnnotationSiqiong Yao, Junchi Yan, Mingyu Wu, et al.
Nature Communications|December 14, 2023
Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsyQiang Gao, Siqiong Yao, Yuan Tian, et al.
Computers in Biology and Medicine|April 18, 2023
A meta-learning approach to improving radiation response prediction in cancersYuening Zhang, Li Qiu, Yongyong Ren, et al.
NPJ Digital Medicine|January 10, 2026
Multimodal intelligent prediction model for in vitro fertilizationQiang Gao, Siqiong Yao, Dan Du, et al.
International Journal of Surgery (London, England)|May 19, 2023
Automated diagnosis and management of follicular thyroid nodules based on the devised small-dataset interpretable foreground optimization network deep learning: a multicenter diagnostic studyZheyu Yang, Siqiong Yao, Yu Heng, et al.
Nature Communications|August 1, 2025
Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imagingPengcheng Shen, Zheyu Yang, Jingjing Sun, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Nucleic Acids Research|October 9, 2023
tRFtarget 2.0: expanding the targetome landscape of transfer RNA-derived fragmentsNingshan Li, Siqiong Yao, Guangjun Yu, et al.
Nature Communications|March 4, 2024
Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule populationSiqiong Yao, Fang Dai, Peng Sun, et al.
IEEE Transactions on Bio-Medical Engineering|August 19, 2025
Classification of Craniomaxillofacial Free Flap: Mechanism and ModelYuhang Men, Jing Han, Siqiong Yao, et al.
Iscience|May 1, 2023
Human understandable thyroid ultrasound imaging AI report system - A bridge between AI and cliniciansSiqiong Yao, Pengcheng Shen, Tongwei Dai, et al.
Frontiers in Bioengineering and Biotechnology|July 7, 2020
Texture Synthesis Based Thyroid Nodule Detection From Medical Ultrasound Images: Interpreting and Suppressing the Adversarial Effect of In-place Manual AnnotationSiqiong Yao, Junchi Yan, Mingyu Wu, et al.
Nature Communications|December 14, 2023
Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsyQiang Gao, Siqiong Yao, Yuan Tian, et al.
Computers in Biology and Medicine|April 18, 2023
A meta-learning approach to improving radiation response prediction in cancersYuening Zhang, Li Qiu, Yongyong Ren, et al.
NPJ Digital Medicine|January 10, 2026
Multimodal intelligent prediction model for in vitro fertilizationQiang Gao, Siqiong Yao, Dan Du, et al.
International Journal of Surgery (London, England)|May 19, 2023
Automated diagnosis and management of follicular thyroid nodules based on the devised small-dataset interpretable foreground optimization network deep learning: a multicenter diagnostic studyZheyu Yang, Siqiong Yao, Yu Heng, et al.
Nature Communications|August 1, 2025
Explainable multimodal deep learning for predicting thyroid cancer lateral lymph node metastasis using ultrasound imagingPengcheng Shen, Zheyu Yang, Jingjing Sun, et al.
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