个人简介

洪瑾，1991年生，江西余干人。中山大学理学博士，英国莱斯特大学联合培养，香港中文大学博士后，2023年入选美国科学研究荣誉学会Sigma Xi正式成员，现南昌大学信息工程学院人工智能系讲师。主持国家自然科学基金项目1项和江西省自然科学基金项目1项，参与国家自然科学基金项目3项。在国内外期刊和会议上发表论文20余篇，以第一/通讯作者在Knowledge-Based Systems，Applied Soft Computing, IEEE Transactions on Consumer Electronics, Computers in Biology and Medicine, Biomedical Signal Processing and Control等SCI期刊上发表论文10余篇，其中ESI高被引论文1篇。任International Journal of Science, Technology and Society期刊编委，Brain-X期刊青年编委，任国际期刊Frontiers in Human Neuroscience, Frontiers in Computational Neuroscience, Journal of Visualized Experiments, BIOCELL, Applied Sciences, Intelligence & Robotics和Technology in Cancer Research & Treatment客座主编/客座编辑。任Information Fusion, Neural Networks, Expert Systems with Applications, Neurocomputing, Computer Methods and Programs in Biomedicine, IEEE Sensors Journal , Biomedical Signal Processing and Control等国际著名期刊审稿人。谷歌学术总被引844，H指数13

研究方向：深度学习，人工智能医疗，医学图像分析

谷歌学术主页：https://scholar.google.com.sg/citations?user=EcxircYAAAAJ&hl=en

联系方式：hongjin@ncu.edu.cn

教育与工作经历

2023.07 - 至今                南昌大学                          讲师                          信息工程学院

2022.10 - 2023.07          广东省人民医院                助理研究员                心血管人工智能与三维技术实验室

2020.11 - 2021.11          香港中文大学                   博士后                       影像与介入放射学系

2018.05 - 2018.11          莱斯特大学                      联合培养                    信息学系 

2017.09 - 2020.06          中山大学                          博士                          构造地质学

2013.09 - 2016.06          中国地质大学（武汉）      硕士                          地质工程

2009.09 - 2013.06          中国地质大学（武汉）      本科                          地球物理学

项目经历

1. 国家自然科学基金委员会, 地区科学基金项目, 62466033, 基于多源无监督域自适应的心脏CT影像分割研究及其在三维可视化手术规划中的应用, 2025-01-01 至 2028-12-31, 32万元, 在研, 主持

2. 江西省自然科学基金委员会, 青年基金项目, 20242BAB20070, 多中心医学图像分割的域自适应方法研究, 2024-06-01 至 2027-05-31, 10万元, 在研, 主持

3. 国家自然科学基金委员会, 面上项目, 82371963, 基于右室重构时空演进异质性的深度学习模型精准预测肺动脉高压所致右心衰竭, 2024-01-01 至 2027-12-31, 48万元, 在研, 参与

4. 国家自然科学基金委员会，面上项目，62276071，基于CT影像的先心病高精度心脏分割算法及其在3D打印手术规划的应用研究，2023-01-01至2026-12-31，53万元，在研，参与

5. 国家自然科学基金委员会，面上项目，42072251，基于岩石微观损伤的破裂失稳研究——从三轴实验同步动态CT观测到数值模拟，2021-01-01至2024-12-31，60万元，在研，参与

6. 儿童发展与学习科学教育部重点实验室（东南大学）, 开放基金, CDLS-2020-03, 基于磁共振影像与深度学习的儿童脑龄精准预测, 2020-12 至 2022-12, 2万元，结题，参与

学术服务

正在征稿的专刊

1. Frontiers in Human Neuroscience (IF=2.4)客座主编：

Special Issue "AI Innovations in Neurological and Psychiatric Disorder Management: Diagnosis to Treatment"截稿日期：2025年5月5日

https://www.frontiersin.org/research-topics/67974

2. JoVE-Journal of Visualized Experiments (IF=1.2)客座主编：

Special Issue "Application of Artificial Intelligence and Innovative Technologies in Ophthalmology"长期征稿

https://app.jove.com/methods-collections/2673

审稿人

Scientific Reports; IEEE Access; Multimedia Tools and Applications; Journal of Computational Science; IEEE Transactions on Artificial Intelligence; Fundamenta Informaticae; International Journal of Cognitive Computing in Engineering; Biomedical Signal Processing and Control; BMC Musculoskeletal Disorders; BMC Oral Health; Applied Sciences; Sustainability; Diagnostics; Computer Methods and Programs in Biomedicine；Physics of Fluids; Expert Systems with Applications; Journal of King Saud University—Computer and Information Sciences; IEEE Open Journal of Engineering in Medicine and Biology; International Journal of Imaging Systems and Technology; Image and Vision Computing; Neurocomputing; Neural Networks; Information Fusion; IEEE Sensors Journal

论文发表

第一/通讯作者

[19]    Qiankun Zuo, Hao Tian*, Yudong Zhang, Jin Hong*. Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis. Computers in Biology and Medicine, 2025, 109898. DOI: 10.1016/j.compbiomed.2025.109898.

[18]    Qiankun Zuo, Zhengkun Shi, Bo Liu, Na Ping, Jiangtao Wang, Xi Cheng, Kexin Zhang, Jia Guo*, Yixian Wu, Jin Hong*. Multi-resolution Visual Mamba with Multi-directional Selective Mechanism for Retinal Disease Detection. Frontiers in Cell and Developmental Biology, 2024, 1484880. DOI: 10.3389/fcell.2024.1484880.

[17]    Zhijiang Wan, Qianhao Yu, Wujie Dai, Siyue Li*, Jin Hong*. Data Generation for Enhancing EEG-Based Emotion Recognition: Extracting Time-Invariant and Subject-Invariant Components with Contrastive Learning. IEEE Transactions on Consumer Electronics, 2024, 3414154. DOI:10.1109/TCE.2024.3414154. 

[16]    Le Gao, Yun Long*, Xin Zhang, Hexing Su, Yong Yu, Jin Hong*. Autism Spectrum Disorders Detection Based on Multi-Task Transformer Neural Network. BMC Neuroscience, 2024, 870. DOI: 10.1186/s12868-024-00870-3. 

[15]    Siyue Li, Shutian Zhao, Yudong Zhang, Jin Hong*, Weitian Chen*. Source-free Unsupervised Adaptive Segmentation for Knee Joint MRI. Biomedical Signal Processing and Control, 2024, 106028. DOI: 10.1016/j.bspc.2024.106028. ESI高被引

[14]    Hexing Su, Le Gao*, Yichao Lu, Han Jing, Jin Hong*, Li Huang, Zequn Chen. Attention-Guided Cascaded Network with Pixel-Importance-Balance Loss for Retinal Vessel Segmentation. Frontiers in Cell and Developmental Biology, 2023, 1196191. DOI: 10.3389/fcell.2023.1196191. 

[13]    Qiankun Zuo, Junhua Hu, Junren Pan, Yu-Dong Zhang*, Changhong Jing, Xuhang Chen, Xiaobo Meng, Jin Hong*. Brain Functional Network Generation with Distribution-regularized Adversarial Graph Autoencoder for Dementia Diagnosis. Computer Modeling in Engineering & Sciences, 2023, 028732. DOI: 10.32604/cmes.2023.028732. 

[12]    Jin Hong, Yu-Dong Zhang, Weitian Chen. Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation. Knowledge-Based Systems, 2022, 109155. DOI: 10.1016/j.knosys.2022.109155. 

[11]    Jin Hong, Simon Chun Ho Yu, Weitian Chen. Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning. Applied Soft Computing, 2022, 121: 108729. DOI: 10.1016/j.asoc.2022.108729. 

[10]   Jin Hong, Zhang-Zhi Feng, Shui-Hua Wang, Andrew Peet, Yu-Dong Zhang, Yu Sun, Ming Yang. Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning. Frontiers in Neurology, 2020. DOI: 10.3389/fneur.2020.584682. 

[9]     Jin Hong, Jie Liu. Rapid estimation of permeability from digital rock using 3D convolutional neural network. Computational Geosciences, 2020, 24: 1523–1539. DOI: 10.1007/s10596-020-09941-w. 

[8]     Jin Hong, Hong Cheng, Yu-Dong Zhang, Jie Liu. Detecting cerebral microbleeds with transfer learning. Machine Vision and Applications, 2019, 30(7): 1123-1133. DOI: 10.1007/s00138-019-01029-5.

[7]     Jin Hong, Hong Cheng, Shui-Hua Wang, Jie Liu. Improvement of cerebral microbleeds detection based on discriminative feature learning. Fundamenta Informaticae, 2019, 168(2-4): 231-248. DOI: 10.3233/FI-2019-1830. 

[6]     Jin Hong, Shui-Hua Wang, Hong Cheng, Jie Liu. Classification of cerebral microbleeds based on fully-optimized convolutional neural network. Multimedia Tools and Applications, 2018, 79: 15151–15169. DOI: 10.1007/s11042-018-6862-z. 

[5]     Jin Hong, Zhi-Hai Lu. Cerebral microbleeds detection via discrete wavelet transform and back propagation neural network, The 2st International Conference on Social Science, Public Health and Education, Atlantis Press, 2018, 228-232. DOI: 10.2991/ssphe-18.2019.54.

[4]     Jin Hong, Liu J. Cerebral Microbleeds Detection via Convolutional Neural Network with and Without Batch Normalization. Frontiers in Intelligent Computing: Theory and Applications. Springer, 2020, 152-162. DOI: 10.1007/978-981-13-9920-6_16. 

[3]     洪瑾, 甘成势, 刘洁. 基于机器学习的洋岛玄武岩主量元素预测稀土元素. 地学前缘, 2019, 26(4): 45-54. DOI:10.13745/j.esf.sf.2019.7.3.

[2]     洪瑾, 甘成势, 刘洁. 基于机器学习的岩石微量元素与主量元素关系初探:以洋岛玄武岩中锆元素为例. 地质科学, 2018, 53(4): 1285-1299. DOI:10.12017/dzkx.2018.074. 

[1]     洪瑾, 昌彦君, 孙帮雄. 基于柯尔-柯尔模型的一维视复电阻率真参数反演. 工程地球物理学报, 2017, 14(3): 271-276. DOI:10.3969/j.issn.1672-7940.2017.03.003.

合作作者

[10]    Rongpei Zhou, Rongfa Li, Yaqian Wu, Jie Chen, Jin Hong, Lisu Yu, Qiegen Liu, Yudong Zhang.Semi-Tensor Product Compressed Sensing with Its Applications: A Review. IEEE Sensors Journal, 2024, 3510033. DOI: 10.1109/JSEN.2024.3510033. 

[9]      Hexing Su, Le Gao, Zhimin Wang, Yong Yu, Jin Hong, Yongqiao Gao. A Hierarchical Full-Resolution Fusion Network and Topology-aware Connectivity Booster for Retinal Vessel Segmentation. IEEE Transactions on Instrumentation & Measurement, 2024, 3411133. DOI: 10.1109/TIM.2024.3411133. 

[8]      Qiankun Zuo, Ruiheng Li, Binghua Shi, Jin Hong, Yanfei Zhu, Xuhang Chen, Yixian Wu, Jia Guo. U-shaped Convolutional Transformer GAN with Multi-resolution Consistency Loss for Restoring Brain Functional Time-series and Dementia Diagnosis. Frontiers in Computational Neuroscience, 2024, 1387004. DOI: 10.3389/fncom.2024.1387004. 

[7]      Xin Zhang, Le Gao, Zhimin Wang, Yong Yu, Yudong Zhang, Jin Hong. Improved neural network with multi-task learning for Alzheimer's disease classification. Heliyon, 2024, e26405. DOI: 10.1016/j.heliyon.2024.e26405. 

[6]      An Zeng, Chunbiao Wu, Wen Xie, Jin Hong, Meiping Huang, Jian Zhuang, Shanshan Bi, Dan Pan, Najeeb Ullah, Kaleem Nawaz Khan, Tianchen Wang, Yiyu Shi, Xiaomeng Li, Guisen Lin, Xiaowei Xu. ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Computerized Medical Imaging and Graphics, 2023, 102287. DOI: 10.1016/j.compmedimag.2023.102287. 

[5]      Yudong Zhang, Jin Hong. Challenges of deep learning in cancers. Technology in Cancer Research & Treatment, 2023. DOI: 10.1177/15330338231173495. 

[4]      Yudong Zhang, Jin Hong, Shuwen Chen. Medical Big Data and Artificial Intelligence for Healthcare. Applied Sciences, 2023, 13(6): 3745. DOI: 10.3390/app13063745. 

[3]      Qiao Deng, Rongli Zhang, Siyue Li, Jin Hong, Yu-Dong Zhang, Winnie Chiu Wing Chu, Lin Shi. Voting based Contour-Aware Framework for Medical Image Segmentation. Applied Sciences, 2023, 13(1): 84. DOI: 10.3390/app13010084.

[2]      Shui-Hua Wang, Jin Hong, Ming Yang. Sensorineural hearing loss identification via nine-layer convolutional neural network with batch normalization and dropout. Multimedia Tools and Applications, 2018, 79: 15135–15150. DOI: 10.1007/s11042-018-6798-3.

[1]      罗传华, 昌彦君, 李志华, 洪瑾, 赵顺杰. 频谱激电法在铜陵市某滑坡地段滑动面勘探中的应用. 工程地球物理学报, 2017, 14(1): 26-30. DOI:10.3969/j.issn.1672-7940.2017.01.005.

学生招募中

欢迎来到我们的课题组！我们有着与众不同的特点，致力于营造一个人文关怀与科研成果并重的环境。以下是我们的特色：

1. 精心指导：我们课题组学生数量有限，这使得我们能够更专注地与每位学生互动。我将亲自为您提供手把手的指导，确保您在科研道路上不再迷茫。

2. 自由灵活：我们课题组的氛围轻松平等，注重的是结果而非过程。这意味着您可以在自己的节奏和地点进行科研工作，只需按照指导完成任务即可。

3. 尊重意愿：我们尊重每位学生的研究兴趣和意愿，绝不强迫您从事不感兴趣的研究方向。您将不会被要求参与与科研无关的杂事，尽情投入到您喜爱的研究中去。

4. 职业规划支持：我们还会为您推荐优秀的硕士导师和博士导师，帮助您做出明智的职业规划选择，指引您的未来发展方向。

5. 学生为本：我们的宗旨是以学生为本，始终坚守学生利益，力行公平公正原则。努力确保每位同学都能够在轻松愉快的氛围中获得丰富的科研经历，收获满满。

如果您是对医学图像分析充满热忱，敢于探索，值得信赖的学生（无论是本科生还是硕士生），欢迎与我联系！:-)