① 影像组学脑网络相关研究成果：

 [1] Yuhao Wang, Yunlong Li, Chenxiao Zhang, Mengxiao Li, Qiang Zheng*, MRomicsNet: A morphomics-radiomics-driven adaptive topological model for AD diagnosis on clinically routine T1-weighted images, Computer Methods and Programs in Biomedicine, vol. 274, pp. 109160, 2026.

[2] Qiang Zheng, Pengzhi Nan, Yongchao Cui, Lin Li, ConnectomeAE: Multimodal Brain Connectome-based Dual-Branch Autoencoder and Its Application in the Diagnosis of Brain Diseases, Computer Methods and Programs in Biomedicine, vol. 267, pp. 108801, 2025.

[3] Limei Song, Zhiwei Song, Pengzhi Nan, Qiang Zheng*, Task-radMBNet: An Improved Task-Driven Dynamic Graph Sparsity Pattern Radiomics-Based Morphological Brain Network for Alzheimer’s Disease Characterization, Brain Connectivity, vol. 15, no. 3, pp. 139-149, 2025.

[4] Chuanzhen Zhu, Honglun Li, Zhiwei Song, Minbo Jiang, Limei Song, Lin Li, Xuan Wang, and Qiang Zheng*, "Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired T1-weighted imaging-based brain network," Health Information Science Systems, vol. 12, no. 1, pp. 19, 2024.

[5] 祝传振, 王璇, 郑强*, “基于元素分离与整体注意力的图卷积网络框架”, 计算机研究与发展, vol. 61, no. 8, pp. 2008-2019, 2024.

[6] Kun Zhao, Qiang Zheng, Martin Dyrba, Timothy Rittman, Ang Li, Tongtong Che, Pindong Chen, Yuqing Sun, Xiaopeng Kang, Qiongling Li, Bing Liu, Yong Liu, Shuyu Li, “Regional radiomics similarity networks reveal distinct subtypes and abnormality patterns in mild cognitive impairment”, Advanced Science, vol. 9, no. 12, pp. 2270073, 2022.

[7] Kun Zhao, Qiang Zheng, Tongtong Che, Martin Dyrba, Qiongling Li, Yanhui Ding, Yuanjie Zheng, Yong Liu, Shuyu Li, “Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis”, Network Neuroscience, vol. 5, no. 3, pp. 783-797, 2021.

② 医学图像生成与脑网络生成相关研究成果：

[1] Zhiwei Song, Chuanzhen Zhu, Minbo Jiang, Minhui Ouyang, Qiang Zheng*, “Predicting functional connectivity network from routinely acquired T1-weighted imaging-based brain network by generative U-GCNet”, Neurocomputing, vol. 511, pp. 128709, 2025.

[2] 张泽华, 赵宁, 王帅, 王璇, 郑强*, 联合掩码引导与多频域双重注意力机制的急性缺血性脑卒中CT到DWI影像生成模型, 电子与信息学报，vol. 47, no. 12, pp.1-11, 2025.

[3] Haiwang Nan, Zhiwei Song, Qiang Zheng*, BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence, Brain Topography, vol 38, pp. 51, 2025.

[4] 王晴, 赵新尧, 刘心月, 邹志孟, 南海旺, 郑强*, Stroke-p2pHD: 脑梗CT到DWI图像的跨模态生成模型, 生物医学工程学杂志, vol. 42, no. 2, pp. 255-262, 2025.

[5] Minbo Jiang, Shuai Wang, Zhiwei Song, Limei Song, Yi Wang, Chuanzhen Zhu, Qiang Zheng*, “Cross2SynNet: cross-device–cross-modal synthesis of routine brain MRI sequences from CT with brain lesion”, Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 37, no. 2, pp. 241-256, 2024.

③ 欧式空间和图空间融合研究成果：

[1] Chenxiao Zhang, Pengzhi Nan, Limei Song, Yuhao Wang, Kaile Su, Qiang Zheng*, MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for brain age estimation, Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 39, pp. 61–79, 2026.

[2] Pengzhi Nan, Lin Li, Zhiwei Song, Yi Wang, Chuanzhen Zhu, Fang Hu, Qiang Zheng*, “A multispatial information representation model emphasizing key brain regions for Alzheimer’s disease diagnosis with structural magnetic resonance imaging”, Quantitative Imaging in Medicine and Surgery, vol. 14, no. 12, pp. 8568–8585, 2024.

[3] Zhiwei Song, Honglun Li, Yiyu Zhang, Chuanzhen Zhu1, Minbo Jiang, Limei Song, Yi Wang, Minhui Ouyang, Fang Hu, Qiang Zheng*, “s2MRI ADNet: an interpretable deep learning framework integrating Euclidean graph representations of Alzheimer’s disease solely from structural MRI”, Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 37, no. 5, pp. 845-857, 2024.

④ 磁共振脑图像分割相关研究成果

[1] Yiyu Zhang, Hongming Li, Qiang Zheng*, “A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer’s disease: deep learning analysis in 3238 participants worldwide”, European Radiology, vol.33, no. 8, pp. 5385-5397, 2023.

[2] Qiang Zheng, Bin Liu, Yan Gao, Lijun Bai, Yu Cheng, Honglun Li, “HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation”, European Journal of Radiology, vol. 162, pp. 110771, 2023.

[3] Xiaolin Jiang, Shuai Wang, Qiang Zheng*, “Deep-learning measurement of intracerebral haemorrhage with mixed precision training: a coarse-to-fine study”, Clinical Radiology, vol. 78, no. 4, pp. se328-e335, 2023.

[4] Qiang Zheng, Yiyu Zhang, Honglun Li, Xiangrong Tong, Minhui Ouyang, “How segmentation methods affect hippocampal radiomic feature accuracy in Alzheimer’s disease analysis?”, European Radiology, vol. 32, no. 10, pp. 6965-6976, 2022.

⑤ 儿科医学图像分析

[1] Mengxiao Li, Jungang Liu, Mingwen Yang, Chenxiao Zhang, Ning Zhao, Zehua Zhang, Qiang Zheng*, Myelination-attention-empowered deep learning model improved brain age prediction in children below 2 years of age, Pediatric Radiology, Online ahead of print, 2026.

[2] Qiang Zheng, Xiaolin Jiang, Jianzheng Sun, Limei Song, Lin Zhang, Jungang Liu, ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children, Computer Methods and Programs in Biomedicine, vol. 276, pp. 109235, 2026.

[3] Yuemei Li, Lin Zhang, Hu Yu, Jian Wang, Shuo Wang, Jungang Liu, Qiang Zheng*, “A comprehensive segmentation of chest X-ray improves deep learning–based WHO radiologically confirmed pneumonia diagnosis in children”, European Radiology, vol. 34, no. 5, pp. 3471-3482, 2024.

[4] Qiang Zheng, Bin Liu, Xiangrong Tong, Jungang Liu, Jian Wang, Lin Zhang, “Automated measurement of leg length discrepancy from infancy to adolescence based on cascaded LLDNet and comprehensive assessment”, Quantitative Imaging in Medicine and Surgery, vol. 13, no. 2, pp. 852, 2023.

[5] Qiang Zheng, Sphoorti Shellikeri, Hao Huang, Misun Hwang, Raymond W Sze, “Deep learning measurement of leg length discrepancy in children based on radiographs”, Radiology, vol. 296, no. 1, pp. 152-158, 2020.

[6] Qiang Zheng, Maxim Itkin, Yong Fan, “Quantification of thoracic lymphatic flow patterns using dynamic contrast-enhanced MR lymphangiography”, Radiology, vol. 296, no. 1, pp. 202-207, 2020.

联合获批教改项目：
[1] 湖南省新工科新医科新农科新文科研究与实践项目：新医科"校企医研四维融通"协同育人体系构建—医工交叉导向的医学影像技术人才培养范式改革
[2] 黑龙江省高等教育教学改革研究项目：基于智慧医疗产业需求的新医科专业产教融合模式构建与实践
[3] 山东省教改项目面上项目：新医科背景下“医工交叉”研究生人才培养的实施路径探索
[4] 教育部产学合作协同育人项目：基于人工智能医学交叉学科的智慧医疗产业技术研究院建设 

  欢迎对AI医疗感兴趣的同学加盟！