主要科研项目 |
1. 科技部, 国家重点研发计划项目-子课题, 2024YFF0907802, 个性化婴幼儿发育检测与指导大模型研究, 2025-04 至 2028-03, 12万元, 在研, 主持 2. 国家自然科学基金委员会, 青年科学基金项目（C类）[原青年科学基金项目], 82202183, 基于迭代式混合增强模型揭示高侵袭性甲状腺乳头状癌超声新特征及其应用机制研究, 2023-01-01 至 2025-12-31, 30万元, 资助期满, 主持 3. 陕西省教育厅, 服务地方专项计划项目, 25JE026, 基于人机混合智能模型探索浅表淋巴结超声影像特征与分子生物学的相关性研究, 2026-01 至 2027-12, 10万元, 在研, 主持 4. 陕西省科学技术厅, 重点研发计划项目, 2023-YBSF-392, 基于可解释性人工智能的甲状腺癌风险分层管理系统的研究, 2023-01 至 2024-12, 6万元, 结题, 主持 5. 陕西省科学技术厅，自然科学基础研究面上项目, 2022JM-324,基于超声影像和病历文本的甲状腺恶性危险分级方法研究, 2022.01.01-2023.12.31, 5万元，结题, 合作主持 6. 国家自然科学基金委员会, 国际(地区)合作与交流项目, W2621020, ERI1介导的miRNA代谢在指/趾形成中的调控机制, 2026-04-01 至 2028-12-31, 15万元, 在研, 参与 7. 国家自然科学基金委员会, 面上项目, 82071952, 基于超声影像方法研究肠道菌群与甲状腺VEGF表达的联合干预对改善桥本甲状腺炎的作用, 2021-01-01 至 2024-12-31, 55万元, 结题, 参与。 8. 陕西省科学技术厅, 重点研发计划项目, 2023-YBSF-513, 基于陕西地区人群乳腺4a类结节患者的多模态影像智能诊断的研究, 2024-01 至 2025-12, 6万元, 结题, 参与 9. 陕西省科学技术厅, 重点研发计划项目, 2023-YBSF-513, 基于陕西地区人群乳腺4a类结节患者的多模态影像智能诊断的研究, 2023-01 至 2024-12, 6万元, 结题, 参与 10. 陕西省科学技术厅, 重点研发计划项目, 2024SF-YBXM-297，基于人工智能深度学习模型的三维超声定量评估胎儿小脑蚓部的研究，2024-01 至 2025-12, 6万元, 结题, 参与 |
代表性论著、科研获奖、人才称号 |
[1] Jingxi Feng, Shaoyi Du*, Heming Xu, Rundong Xue, Xiangmin Han, Dong Zhang, Jue Jiang, Yue Gao, Juan Wang*, "Topology-Aware Multi-View Hypergraph Computation-Based Cross-Modal Brain Network Fusion for Brain Disease Diagnosis," Information Fusion, vol. 127, p. 103751, 2026. (SCI, EI) [2] Hongcheng Han, Zhiqiang Tian, Minghao Wang, Yutong Zhang, Dong Zhang, Qinbo Guo, Jue Jiang, Hui Guo, Shaoyi Du*, Juan Wang*, "AsyCMST: Asymmetric Cross-Modal Spatio-Temporal Learning for Multimodal Ultrasound Nodule Recognition," Medical Image Analysis, vol. 112, p. 104127, 2026. (SCI, EI) [3] Rundong Xue, Shaoyi Du*, Xiangmin Han, Dong Zhang, Junchang Li, Juan Wang*, "HOBN: A General Multi-View High-Order Brain Network Learning Framework for Brain Disease Diagnosis," Expert Systems with Applications, vol. 331, p. 133285, 2026. (SCI, EI) [4] Minghao Wang, Shaoyi Du*, Huanhuan Huo, Jue Jiang, Dong Zhang, Hongcheng Han, Shengdi Hou, Juan Wang*, "Keypoint-Guided Medical Video Segmentation Model with Spatiotemporal Feature Fusion," IEEE Transactions on Medical Imaging, vol. 45, no. 6, pp. 3380-3393, 2026. (SCI, EI) [5] Yuping Lin, Jingxi Feng, Xudong Chen, R.undong Xue, Jue Jiang, Zhiqiang Tian, and Juan Wang*. Multi-Level Graph Self-Supervised Learning for Multi-Modal Medical Corpus Construction. Pattern Recognition, vol. 171，no. 112113, 2026. (SCI, EI) [6] Hao Hu#, Rundong Xue#, Shaoyi Du*, Xiangmin Han, Jingxi Feng, Zeyu Zhang, Wei Zeng, Yue Gao, Juan Wang*, "Exploring Dynamic Interpretable Brain Networks via Hierarchical Graph Transformer," Pattern Recognition, vol. 178, p. 113371, 2026. (SCI, EI) [7] Yu-Tong Zhang#, Si-Yi Wu#, Dong Zhang, Zheng-Yi Yang, Sheng-Wei Zhao, Hong-Cheng Han, Xin Yuan, Li-Rong Wang, Jiang Jue, Shao-Yi Du, Qi Zhou*, Juan Wang*, "Evaluating Chain-of-Thought Reasoning in Large Language Models for Thyroid Ultrasound Interpretation: A Dual-Information Approach," Frontiers in Artificial Intelligence, vol. 9, p. 1780373, 2026. (SCI, ESCI) [8] Wang L, Zhang L, Chen J, Wang D, Zhang Y, Sun L, Su W, Li M, Zhou Q, Wang J*, Jiang J*. CD8+ T Cells in Hashimoto's Thyroiditis-Associated Papillary Thyroid Carcinoma. Eur Thyroid J. 2026 May 6:ETJ-25-0365. [9] Hongcheng Han, Zhiqiang Tian, Qinbo Guo, Jue Jiang, Shaoyi Du*, and Juan Wang*, "HSC-T: B-Ultrasound-to-Elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis," IEEE Journal of Biomedical and Health Informatics, 29( 2), pp. 799-806, 2025.02.23. (SCI, EI) [10] Lin zhang, Lirong Wang, Runa Liang, Xin He, Dan Wang, Lei Sun, Shanshan Yu, Wenxiu Su, Wei Zhang, Qi Zhou*, Juan Wang*, Jue Jiang*. An effective ultrasound features-baseddiagnostic model via principal component analysis facilitated differentiating subtypes of mucinous breast cancer from fibroadenomas, Clin Breast Cancer. 2024. [11] Wang J, Dong C, Zhang YZ, Wang L, Yuan X, He M, Xu S, Zhou Q*, Jiang J*. A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients. Eur Radiol, 2023. (Q1, IF: 5.7, 第一) [12] Wang J; Jiang J; Zhang D; Zhang YZ; Guo L; Jiang Y; Du S*; Zhou Q*. An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules, Eur Radiol , 2022.Mar, 32(3): 2120-2129. [13] 林玉萍,赵祎学,郭晓茹,井佳瑜,袁新,姜珏,王娟..多模态超声影像预测模型在浅表淋巴结疾病中的鉴别诊断价值.中国超声医学杂志, (2025), 41(S1),7-12. [14] 赵祎学,侯慧垚,曹茹,李娜,许金枝,何鑫,王娟.超声和超声造影特征在结内型淋巴瘤与转移性淋巴结中的差异及诊断价值.中华实用诊断与治疗杂志, (2025),39(11),1031-1037. [15] 袁新,张雨彤,王理蓉,曹茹,姜珏,周琦,王娟. 基于多模态超声数据模型对比和优化ACR TI-RADS分类系统[J]. 中国超声医学杂志,2025,41(5):498-501. [16] 陈阿倩,曹茹,李娜,袁新,王理蓉,姜珏,周琦,王娟. 可解释性人工智能超声影像特征风险模型预测甲状腺乳头状癌颈部淋巴结转移的价值[J]. 中华超声影像学杂志,2024,33(1):14-20. [17] 王娟,陈阿倩,梁汝娜,何鑫,王莎莎,姜珏,周琦. 基于常规超声与超声造影特征构建列线图用于囊实性甲状腺癌风险预测的研究[J]. 中国超声医学杂志,2023,39(3):256-260. [18] 王娟,梁汝娜,余珊珊,何鑫,陈阿倩,侯慧垚,井佳瑜,张瑾晖,周琦,姜珏. 基于超声造影定量参数联合常规超声图像特征的诊断模型鉴别诊断肉芽肿性小叶性乳腺炎与早期浸润性乳腺癌的价值[J]. 临床超声医学杂志,2023,25(5):345-350. [19] 梁汝娜,张瑾晖,何鑫,余珊珊,姜珏,周琦,王娟. 不同大小BI-RADS4类乳腺纤维腺瘤的常规超声及超声造影特征分布[J]. 中国超声医学杂志,2023,39(7):755-759. [20] 边锦霞,姜珏,何鑫,曹茹,梁汝娜,周琦,王娟. 医准智能软件在4类乳腺结节良恶性鉴别诊断中的应用价值[J]. 中国超声医学杂志,2023,39(7):750-754. |
