所属学科专业：控制科学与工程、计算机科学与技术、信息与通信工程

导师简介：

魏炜，1984年生，男，工学博士，副教授，硕士研究生导师，电子信息学院智能信息处理研究所所长。本科毕业于西北大学计算机科学与技术专业，博士毕业于西安电子科技大学信息与通信工程专业（中国科学院自动化研究所联合培养），中国科学院自动化研究所和北京航空航天大学访问学者。长期从事人工智能的教学以及智慧医疗领域的科研工作，致力于人工智能技术在医疗领域的临床转化。先后主持国家级、省部级、厅局级以及企业合作项目10余项，累计项目经费200余万元；发表学术论文40余篇；担任Medical Image Analysis、Computers in Biology and Medicine等中科院1区（TOP）SCI期刊审稿人。

主要研究方向：人工智能，医学图像处理，医药大模型

近年来主要科研项目：

1. 国家自然科学基金面上项目，数字病理引导下的术前MRI精准预测胶质母细胞瘤相关分子标志物的研究，2024-01至今，国家级，立项经费50万元，合作单位主持；

2. *****生理信号*****监控预警系统，国防军工项目，2024-11至今，56万元，主持；

3. 陕西省自然科学基础研究计划面上项目，基于影像病理组学的HER2阳性乳腺癌新辅助疗效不敏感人群预测研究，2023.01-2024.07，省部级，主持；

4. 陕西省自然科学基础研究计划一般项目，影像组学在高级别浆液性卵巢癌术后复发风险分析中的应用，2020.01-2021.12，省部级，主持；

5. 西安市科技计划项目，基于深度学习影像组学的肺癌放疗疗效评估和副反应预测辅助诊断系统，2022.07-2024.06，厅局级，主持；

6. 陕西省教育厅自然科学专项科研计划，脑胶质瘤影像组学及临床应用的研究，2017.01-2018.09，厅局级，主持。

7. 西安市碑林区科技计划项目，基于联邦学习的分布式医学影像基础模型训练平台，2024-01至今，主持。

近年来主要科研成果：

1. 乳腺癌磁共振成像病灶自动分割系统开发（一期、二期、三期），146万元，校企合作，成果转化；

2. 基于深度学习的影像组学在肺癌放疗疗效评估和副反应预测辅助诊断系统，8万元，校企合作，成果转化。

发表论文：

(1) A multi-scale, multi-task fusion UNet model for accurate breast tumor segmentation. Computer Methods and Programs in Biomedicine, 2024, 258(2025): 108484. (中科院二区，IF=7.027)

(2) Effects of one night of sleep deprivation on whole brain intrinsic connectivity distribution using a graph theory neuroimaging approach. Sleep Medicine, 2024, 125(2025): 89–99. (中科院二区，IF=3.800)

(3) Development of Prognostic Biomarkers by TMB-Guided WSI Analysis: A Two-Step Approach. IEEE Journal of Biomedical and Health Informatics, 2023, 27(4): 1780-1789. (中科院一区，IF=7.700)

(4) Tumor Mutation Burdene-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning. American Journal of Pathology, 2023, 193(12): 2111-2121. (中科院二区，IF=6.000)

(5) Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319. (ESI高被引，中科院一区，IF=36.615)

(6) Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer. Computer Methods and Programs in Biomedicine, 2022: 106914. (中科院二区，IF=7.027)

(7) Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective. Human Brain Mapping, 2022, 43(12): 3824-3839. (中科院二区，IF=5.399)

(8) An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions, Frontiers in Oncology, 2022, 11: 733260. (中科院二区，IF=5.738)

(9) A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions. European Journal of Radiology, 2022, 149: 110187. (中科院二区，IF=4.531)

(10) Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine, 2021: 103442. (中科院1区，IF=11.205)

(11) Neuroimaging Phenotyping and Assessment of Structural- Metabolic- Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa. Journal of Magnetic Resonance Imaging, 2021, 54(3): 925-935. (中科院二区，IF=5.119)

(12) Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study, Frontiers in Oncology, 2020, 10: 235. (中科院二区，IF=5.738)

(13) Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma , Radiotherapy and Oncology, 2019, 141: 239-246. (中科院一区，IF=6.901)

(14) Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer. Radiotherapy and Oncology, 2019, 132: 171-177. (中科院一区，IF=6.901)

(15) A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: A multicenter study, Frontiers in Oncology, 2019, 9: 255. (中科院二区，IF=5.738)

(16) A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients with Glioma. Frontiers in Oncology, 2019, 9: 1183. (中科院二区，IF=5.738)

(17) Esculetin improves cognitive impairments induced by transient cerebral ischaemia and reperfusion in mice via regulation of mitochondrial fragmentation and mitophagy. Behavioural Brain Research, 2019, 327(2019): 112007. (中科院二区, IF=3.352)

(18) Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications, European Journal of Radiology, 2019, 121: 108711. (中科院二区，IF=4.531)

(19) Radiomic Analysis of Multiparametric Magnetic Resonance Imaging for Differentiating Skull-base Chordoma and Chondrosarcoma. European Journal of Radiology, 2019, 118: 81-87. (中科院二区，IF=4.531)

(20) Radiomics: A Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, 4130-4133. (EI: 20184906171667)

(21) A Novel MRI-Based Radiomics Model for Predicting Recurrence in Chordoma. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, 139-142. (EI: 20184906171530)

(22) Unsupervised Deep Learning Features for Lung Cancer Overall Survival Analysis. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, 2583-2586. (EI: 20184906171844)

(23) Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. Journal of Magnetic Resonance Imaging, 2018, 47(5): 1380-1387. (ESI高被引，中科院二区，IF=5.119)

(24) 魏炜, 何坤山, 胡振远等. 人工智能在结直肠癌诊疗中的研究进展及前景展望. 中华胃肠外科杂志, 2024, 27(1): 15-23. (CSCD核心库，约稿，专家述评)

(25) 胡振远, 魏炜, 胡文鐘等. 基于磁共振多参数图像融合及DenseNet网络的脑胶质瘤IDH突变预测研究. 磁共振成像, 2023, 14(7): 10-17. (CSCD核心库，约稿，本科生第一作者)

(26) 魏炜, 刘振宇, 王硕, 田捷*. 影像组学技术研究进展及其在结直肠癌中的临床应用. 中国生物医学工程学报, 2018, 37(5): 513-520. (CSCD核心库，约稿，综述)

(27) 马梦航, 魏炜, 刘振宇, 田捷*. 人工智能在结直肠癌医学影像中的临床应用. 肿瘤影像学, 2022, 3(2): 97-104. (核心期刊，约稿，综述)

(28) 魏炜, 刘振宇, 田捷*. 影像组学在胶质瘤临床诊疗中的应用现状. 中国微侵袭神经外科杂志, 2018, 23(6): 277-280. (核心期刊，约稿，综述)

(29) Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis. The Innovation Medicine, 2024, 2(2): 100069. (约稿，综述)

(30) Advances in the Application of Radiomics in Colorectal Cancer. Chinese Journal of Biomedical Engineering, 2021, 30(1). (约稿，综述)

(31) A systematic review on investigating major depressive disorder and bipolar disorder using MRI and genetic data from 2018 to 2024. Brain-X, 2024, 2(3): e70000. (约稿，系统综述)

(32) Research Progress of Artificial Intelligence in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. EngMedicine, 2024, 1(2). (约稿，综述，封面文章)

联系方式：Email: weiwei@xpu.edu.cn; QQ: 58699445