1.           Zhenwei Wang, Mengshen He, Yifan Lv, Enjie Ge, Shu Zhang, Ning Qiang, Tianming Liu, Fan Zhang, Xiang Li, and Bao Ge, Accurate corresponding fiber tract segmentation via FiberGeoMap learner with application to autism. Cerebral Cortex, 2023: p. bhad125.

2.           Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng  Liu, Xiang  Li, Bao  Ge, Dajiang  Zhu, Yixuan  Yuan, Dinggang  Shen, Tianming  Liu, and Shu  Zhang, Prompt engineering for healthcare: Methodologies and applications. arXiv preprint arXiv:2304.14670, 2023.

3.           Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, and Shu Zhang, Review of large vision models and visual prompt engineering. arXiv preprint arXiv:2307.00855, 2023.

4.           Saed Rezayi, Zhengliang Liu, Zihao Wu, Chandra Dhakal, Bao Ge, Haixing Dai, Gengchen Mai, Ninghao Liu, Chen Zhen, and Tianming Liu, Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications. arXiv preprint arXiv:2306.11892, 2023.

5.           Ning Qiang, Jie Gao, Qinglin Dong, Huiji Yue, Hongtao Liang, Lili Liu, Jingjing Yu, Jing Hu, Shu Zhang, and Bao Ge, Functional brain network identification and fMRI augmentation using a VAE-GAN framework. Computers in Biology and Medicine, 2023: p. 107395.

6.           Ning Qiang, Jie Gao, Qinglin Dong, Jin Li, Shu Zhang, Hongtao Liang, Yifei Sun, Bao Ge, Zhengliang Liu, and Zihao Wu, A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks. Behavioural Brain Research, 2023. 452: p. 114603.

7.           Yifan Lv, Zili Kang, Tianle Han, Mengshen He, Ruhai Du, Tuo Zhang, Tianming Liu, and Bao Ge, Cerebral cortical regions always connect with each other via the shortest paths. Cerebral Cortex, 2023: p. bhad197.

8.           Zhengliang Liu, Tianyang Zhong, Yiwei Li, Yutong Zhang, Yi Pan, Zihao Zhao, Peixin Dong, Chao Cao, Yuxiao Liu, Peng Shu, Yaonai Wei, Zihao Wu, Chong Ma, Jiaqi Wang, Sheng Wang, Mengyue Zhou, Zuowei Jiang, Chunlin Li, Shaochen Xu, Lu Zhang, Haixing Dai, Kai Zhang, Xu Liu, Lin Zhao, Peilong Wang, Pingkun Yan, Jun Liu, Bao Ge, Lichao Sun, Dajiang Zhu, Xiang Li, Wei Liu, Xiaoyan Cai, Xintao Hu, Xi Jiang, Shu Zhang, Xin Zhang, Tuo Zhang, Shijie Zhao, Quanzheng Li, Hongtu Zhu, Dinggang Shen, and Tianming Liu, Evaluating Large Language Models for Radiology Natural Language Processing. arXiv preprint arXiv:2307.13693, 2023.

9.          Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, and Bao Ge, Summary of chatgpt/gpt-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852, 2023.

10.        Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Zhao Lin, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, and Bao Ge, Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models. Meta-Radiology, 2023: p. 100017.

11.         Yiheng Liu, Enjie Ge, Ning Qiang, Tianming Liu, and Bao Ge. Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks. in 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). 2023. IEEE.

12.         Zili Kang, Yifan Lv, Mengshen He, Yiheng Liu, Tianming Liu, and Bao Ge. Brain Surface Can Predict Fiber Connections. in 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). 2023. IEEE.

13.         Xinlei Jia, Yali Peng, Bao Ge, Jun Li, Shigang Liu, and Wenan Wang, A multi-scale dilated residual convolution network for image denoising. Neural Processing Letters, 2023. 55(2): p. 1231-1246.

14.         Mengshen He, Xiangyu Hou, Enjie Ge, Zhenwei Wang, Zili Kang, Ning Qiang, Xin Zhang, and Bao Ge, Multi-head attention-based masked sequence model for mapping functional brain networks. Frontiers in Neuroscience, 2023. 17: p. 1183145.

15.         Zhenwei Wang, Yifan Lv, Mengshen He, Enjie Ge, Ning Qiang, and Bao Ge. Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022. Springer Nature Switzerland Cham.

16.         Saed Rezayi, Zhengliang Liu, Zihao Wu, Chandra Dhakal, Bao Ge, Chen Zhen, Tianming Liu, and Sheng Li. Agribert: knowledge-infused agricultural language models for matching food and nutrition. in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2022.

17.         Ning Qiang, Qinglin Dong, Hongtao Liang, Jin Li, Shu Zhang, Cheng Zhang, Bao Ge, Yifei Sun, Jie Gao, and Tianming Liu, Learning brain representation using recurrent Wasserstein generative adversarial net. Computer Methods and Programs in Biomedicine, 2022. 223: p. 106979.

18.         Ning Qiang, Qinglin Dong, Hongtao Liang, Bao Ge, Shu Zhang, Cheng Zhang, Jie Gao, and Yifei Sun, A novel ADHD classification method based on resting state temporal templates (RSTT) using spatiotemporal attention auto-encoder. Neural Computing and Applications, 2022. 34(10): p. 7815-7833.

19.         Zhengliang Liu, Mengshen He, Zuowei Jiang, Zihao Wu, Haixing Dai, Lian Zhang, Siyi Luo, Tianle Han, Xiang Li, Xi Jiang, Dajiang Zhu, Xiaoyan Cai, Bao Ge, Wei Liu, Jun Liu, Dinggang Shen, and Tianming Liu, Survey on natural language processing in medical image analysis. Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical Sciences, 2022. 47(8): p. 981-993.

20.         Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu, Shijie Zhao, Xintao Hu, Dajiang Zhu, Tianming Liu, and Bao Ge, Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention. arXiv preprint arXiv:2205.09576, 2022.

21.         Xinlei Jia, Yali Peng, Jun Li, Yunhong Xin, Bao Ge, and Shigang Liu, Pyramid dilated convolutional neural network for image denoising. Journal of Electronic Imaging, 2022. 31(2): p. 023024-023024.

22.         Mengshen He、Xiangyu Hou、Zhenwei Wang、Zili Kang、Xin Zhang、Ning Qiang、Bao Ge, Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 2022.

23.         Ning Qiang, Qinglin Dong, Hongtao Liang, Bao Ge, Shu Zhang, Yifei Sun, Cheng Zhang, Wei Zhang, Jie Gao, and Tianming Liu, Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder. Journal of neural engineering, 2021. 18(4): p. 0460b6.

24.         Xinlei Jia, Yali Peng, Jun Li, Bao Ge, Yunhong Xin, and Shigang Liu, Dual-complementary convolution network for remote-sensing image denoising. IEEE Geoscience and Remote Sensing Letters, 2021. 19: p. 1-5.

25.         Huan Wang, Qinglin Dong, Ning Qiang, Xin Zhang, Tianming Liu, and Bao Ge. Task fMRI guided Fiber clustering via a deep clustering method. in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020. IEEE.

26.         Ning Qiang, Bao Ge, Qinglin Dong, Fangfei Ge, and Tianming Liu. Neural architecture search for optimizing deep belief network models of fMRI data. in Multiscale Multimodal Medical Imaging: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 1. 2020. Springer.

27.         Ning Qiang, Qinglin Dong, Wei Zhang, Bao Ge, Fangfei Ge, Hongtao Liang, Yifei Sun, Jie Gao, and Tianming Liu, Modeling task-based fMRI data via deep belief network with neural architecture search. Computerized Medical Imaging and Graphics, 2020. 83: p. 101747.

28.         Ning Qiang, Qinglin Dong, Yifei Sun, Bao Ge, and Tianming Liu. deep variational autoencoder for modeling functional brain networks and ADHD identification. in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020. IEEE.

29.         Ning Qiang, Qinglin Dong, Fangfei Ge, Hongtao Liang, Bao Ge, Shu Zhang, Yifei Sun, Jie Gao, and Tianming Liu, Deep variational autoencoder for mapping functional brain networks. IEEE Transactions on Cognitive and Developmental Systems, 2020. 13(4): p. 841-852.

30.         Bao Ge, Huan Wang, Panpan Wang, Yin Tian, Xin Zhang, and Tianming Liu, Discovering and characterizing dynamic functional brain networks in task FMRI. Brain Imaging and Behavior, 2020. 14: p. 1660-1673.

31.         Bao Ge, Xiang Li, Xi Jiang, Yifei Sun, and Tianming Liu, A dictionary learning approach for signal sampling in task-based fMRI for reduction of big data. Frontiers in Neuroinformatics, 2018. 12: p. 17.

32.         Yu Zhao, Hanbo Chen, Yujie Li, Jinglei Lv, Xi Jiang, Fangfei Ge, Tuo Zhang, Shu Zhang, Bao Ge, and Cheng Lyu, Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder. NeuroImage: Clinical, 2016. 12: p. 23-33.

33.         Shijie Zhao, Junwei Han, Xi Jiang, Xintao Hu, Jinglei Lv, Shu Zhang, Bao Ge, Lei Guo, and Tianming Liu. Exploring auditory network composition during free listening to audio excerpts via group-wise sparse representation. in 2016 IEEE International Conference on Multimedia and Expo (ICME). 2016. IEEE.

34.         Bao Ge, Milad Makkie, Jin Wang, Shijie Zhao, Xi Jiang, Xiang Li, Jinglei Lv, Shu Zhang, Wei Zhang, and Junwei Han, Signal sampling for efficient sparse representation of resting state FMRI data. Brain imaging and behavior, 2016. 10: p. 1206-1222.

35.         Shijie Zhao, Junwei Han, Jinglei Lv, Xi Jiang, Xintao Hu, Yu Zhao, Bao Ge, Lei Guo, and Tianming Liu, Supervised dictionary learning for inferring concurrent brain networks. IEEE transactions on medical imaging, 2015. 34(10): p. 2036-2045.

36.         Tuo Zhang, Hanbo Chen, Xi Jiang, Bao Ge, Lei Guo, and Tianming Liu. Group-wise consistent sulcal fundi segmentation based on DMRI-derived ODF features. in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015. IEEE.

37.         Shu Zhang, Xiang Li, Jinglei Lv, Xi Jiang, Bao Ge, Lei Guo, and Tianming Liu. Characterizing and differentiating task-based and resting state FMRI signals via two-stage dictionary learning. in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015. IEEE.

38.         Milad Makkie, Shijie Zhao, Xi Jiang, Jinglei Lv, Yu Zhao, Bao Ge, Xiang Li, Junwei Han, and Tianming Liu, HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI). Brain informatics, 2015. 2: p. 225-238.

39.         Ke Jing, Tuo Zhang, Jianfeng Lu, Hanbo Chen, Xi Jiang, Lei Guo, Longchuan Li, Xiaoping Hu, Jinglei Lv, and Bao Ge. Multiscale and multimodal fusion of tract-tracing and DTI-derived fibers in macaque brains. in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015. IEEE.

40.        Juanli Han, Gangqiang Zhu, Mirabbos Hojamberdiev, Jianhong Peng, Xi Zhang, Yun Liu, Bao Ge, and Peng Liu, Rapid adsorption and photocatalytic activity for Rhodamine B and Cr (VI) by ultrathin BiOI nanosheets with highly exposed {001} facets. New Journal of Chemistry, 2015. 39(3): p. 1874-1882.

41.         Fangfei Ge, Jinglei Lv, Xintao Hu, Bao Ge, Lei Guo, Junwei Han, and Tianming Liu. Deriving ADHD biomarkers with sparse coding based network analysis. in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015. IEEE.

42.         Bao Ge, Yin Tian, Xintao Hu, Hanbo Chen, Dajiang Zhu, Tuo Zhang, Junwei Han, Lei Guo, and Tianming Liu, Construction of multi-scale consistent brain networks: methods and applications. PloS one, 2015. 10(4): p. e0118175.

43.         Tuo Zhang, Dajiang Zhu, Xi Jiang, Bao Ge, Xintao Hu, Junwei Han, Lei Guo, and Tianming Liu, Predicting cortical ROIs via joint modeling of anatomical and connectional profiles. Medical image analysis, 2013. 17(6): p. 601-615.

44.         Bao Ge, Lei Guo, Dajiang Zhu, Tuo Zhang, Xintao Hu, Junwei Han, and Tianming Liu. Construction of multi-scale common brain networks based on DICCCOL. in Information Processing in Medical Imaging: 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28–July 3, 2013. Proceedings 23. 2013. Springer.

45.         Bao Ge, Lei Guo, Tuo Zhang, Dajiang Zhu, Xintao Hu, Junwei Han, and Tianming Liu. Construction of multi-scale brain networks via DICCCOL landmarks. in 2013 IEEE 10th International Symposium on Biomedical Imaging. 2013. IEEE.

46.         Bao Ge, Lei Guo, Tuo Zhang, Xintao Hu, Junwei Han, and Tianming Liu, Resting state fMRI-guided fiber clustering: methods and applications. Neuroinformatics, 2013. 11: p. 119-133.

47.         Bao Ge, Lei Guo, Tuo Zhang, Dajiang Zhu, Kaiming Li, Xintao Hu, Junwei Han, and Tianming Liu. Group-wise consistent fiber clustering based on multimodal connectional and functional profiles. in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III 15. 2012. Springer Berlin Heidelberg.

48.         Bao Ge, Lei Guo, Jinglei Lv, Xintao Hu, Junwei Han, Tuo Zhang, and Tianming Liu. Resting state fMRI-guided fiber clustering. in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part II 14. 2011. Springer.

49.         Bao Ge, Lei Guo, Kaiming Li, Hai Li, Carlos Faraco, Qun Zhao, Stephen Miller, and Tianming Liu. Automatic clustering of white matter fibers based on symbolic sequence analysis. in Medical Imaging 2010: Image Processing. 2010. SPIE.

6. 硕士刘宜衡，韩甜乐为共同一作的论文（Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models）被 Meta-Radiology接收。（2023.8.10）

5. 硕士生吕怡帆为第一作者的论文（Cerebral cortical regions always connect with each other via the shortest paths）被神经科学顶刊Cerebral Cortex接收。（2023.5.15）

4. 硕士生贺孟申为第一作者的论文（Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks）被神经科学顶刊frontiers in Neuroscience接收。（2023.4.17）

3. 硕士生王振威为第一作者的论文（Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner with Application to Autism）被神经科学顶刊Cerebral Cortex接收。（2023.3.17）

2. 实验室2篇论文被ISBI 2023接收（2023.2.13）

实验室2名硕士生（康子力，刘宜衡）为第一作者的论文被ISBI2023接收。IEEE国际生物医学成像研讨会（ISBI，International Symposium on Biomedical Imaging）是一个专门针对生物和生物医学成像的数学、算法和计算方面的学术会议，涵盖所有的可观测尺度，促进了不同成像领域之间的知识转移，并为生物医学成像的方法做出了贡献，由 IEEE 信号处理学会和 IEEE 医学与生物学工程学会联合举办。 ISBI 2023 将于 2023 年 4 月 18 日至 21 日在拉丁美洲哥伦比亚卡塔赫纳举行。

1. 实验室2篇文章被MICCAI 2022接收（2022.6.3）

实验室2名硕士生(王振威，贺孟申)为第一作者的论文被MICCAI2022接收。MICCAI的全称是International Conference on Medical Image Computing and Computer Assisted Intervention，是医学图像分析领域的顶级国际会议。MICCAI，ISBI，IPMI为医学图像领域的3大顶会。MICCAI会议具有以下特点：高度国际化（134所全球顶级科研高校的世界权威研究团队）、覆盖范围广（智能化医学检测、诊断与治疗领域，聚焦热点技术、关键理论、重大疾病应用与交叉融合领域，覆盖了计算病理学、脑疾病诊断、超声成像分析、智能化手术引导等多个领域）、学科前沿交叉（不仅关注疾病诊断，更强调疾病智能化的治疗引导，如智能化的放射治疗以及基于增强现实的手术引导策略等重点领域，同时将在深度学习、迁移学习、统计图谱、域自适应等热点方向开展专题研讨）以及多元化交流等。迄今已经举办了24届。第25届MICCAI会议将于2022年9月在新加坡召开，本次会议共收到投稿1800余篇，往年的录用率通常在30%以内。

以下为2篇入选论文科研成果概述：

1）Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner

Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clustering or classification. Among them, how to define similarity is the basic premise and assumption of the whole method, and determines its potential reliability and application. The similarity features defined by previous studies ranged from geometric to anatomical, and then to functional characteristics, accordingly, the resulting fiber tracts seem more and more meaningful, while their reliability declined. Therefore, here we still adopt geometric feature for fiber tract segmentation, and put forward a novel descriptor (FiberGeoMap) for representing fiber’s geometric feature, which can depict effectively the shape and position of fiber, and can be inputted into our revised Transformer encoder network, called as FiberGeoMap Learner, which can well fully leverage the fiber’s features. Experimental results showed that the FiberGeoMap combined with FiberGeoMap Learner can effectively express fiber’s geometric features, and can differentiate the various fiber tracts, furthermore, the common fiber tracts among individuals can be identified by this method, thus avoiding additional image registration. The comparative experiments demonstrated that the proposed method had better performance than the existing methods. The code is openly available at https://github.com/Garand0o0/FiberTractSegmentation.

2）Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks

It has been of great interest in the neuroimaging community to discover brain functional networks (FBNs) based on task functional magnetic resonance imag-ing (tfMRI). A variety of methods have been used to model tfMRI sequences so far, such as recurrent neural network (RNN) and Autoencoder. However, these models are not designed to incorporate the characteristics of tfMRI sequences, and the same signal values at different time points in a fMRI time series may rep-resent different states and meanings. Inspired by cloze learning methods and the human ability to judge polysemous words based on context, we proposed a self-supervised a Multi-head Attention-based Masked Sequence Model (MAMSM), as BERT model uses (Masked Language Modeling) MLM and multi-head atten-tion to learn the different meanings of the same word in different sentences. MAMSM masks and encodes tfMRI time series, uses multi-head attention to cal-culate different meanings corresponding to the same signal value in fMRI se-quence, and obtains context information through MSM pre-training. Furthermore this work redefined a new loss function to extract FBNs according to the task de-sign information of tfMRI data. The model has been applied to the Human Con-nectome Project (HCP) task fMRI dataset and achieves state-of-the-art perfor-mance in brain temporal dynamics, the Pearson correlation coefficient between learning features and task design curves was more than 0.95, and the model can extract more meaningful network besides the known task related brain networks.

研究方向为脑影像，通过神经影像（T1、T2、DTI、fMRI 等）手段，探究大脑的结构和功能，并对大脑疾病、认知和教育研究提供方法和工具。

致有意向的硕士报考生：

此方向对编程要求较高，工具涉及python, matlab, linux等，知识背景涉及人工智能、深度学习、图像处理等。

我们做的事情基本可以在科研界和工业界无缝转换。

报考硕士要求：诚实、勤奋、认真、坚持、主动、积极，有团队意识；对科研有兴趣，有良好的编程基础。

欢迎各位学子加入我们的团队。