• 钱晓华

    长聘教轨副教授、博士生导师。曾为美国德克萨斯大学生物医学信息学院助理教授;还先后任职于中科院上海高研院高端医疗影像技术研究中心和美国维克森林大学医学院。吉林大学电子工程和美国杜克大学医学物理专业联合培养博士。主要研究兴趣是医学图像处理与分析,计算机视觉,以及健康大数据挖掘与分析。目前主持国自然面上、上海市面上、企业横向以及医工交叉等课题;作为骨干参与张江重大项目和上海交大重大项目STAR计划等;并以第一作者/通讯作者发表Nature Communications, Med Image Anal, IEEE Trans. Med Imaging, IEEE Trans. Multimedia, IEEE Trans. Fuzzy Syst., IEEE Trans. Neural Syst. Rehabilitation Eng., IEEE J Biomed Health Inform, Neurocomputing, Med. Phys.等领域顶尖和知名学术期刊论文数十篇。

    实验室招生(聘)信息

    如果心态积极,具有科研热情、自律能力和自我驱动力;并且想拥有高水平的科研能力和科研成果,最终养成较高的科研素养和品味;那欢迎加入实验室:

    1)欢迎2022年夏令营学生咨询;

    2)欢迎应聘医疗AI方向博士后。

研究方向

1. 主要研究兴趣:

医学图像处理与分析,机器学习与深度学习(图神经网络)的算法研究;包括图像(视频)的细粒度分类与预测,医学图像的检测与分割,以及健康大数据挖掘与分析。主要解决的技术挑战:小样本分析,多模态多来源(域)数据的融合与泛化,可解释性分析和高维数据挖掘。

2. 主要研究课题:

1)胰腺癌和脑胶质瘤诊断和治疗(手术相关)的临床全过程智能算法研究,包括脑胶质瘤术后真假进展诊断,胰腺癌早期筛查,胰腺癌多模态影像检测与分割,胰腺癌淋巴转移/良恶性的分析和预测,与胰腺癌手术可切除性分析等。

2)退行性疾病视频运动功能评估的核心算法研究,以及在临床诊断与评估、远程医疗和居家管理等方面的应用。

代表性论文专著


1.  R. Guo#, J. Sun#, C. Zhang, X. Qian*. A Self-Supervised Metric Learning Framework for the Arising-from-Chair Assessment of Parkinsonians with Graph Convolutional Networks. IEEE Transactions on Circuits and Systems for Video Technology, 03, 2022.

2.  J. Li#, L. Qi#, Q. Chen, Y. Zhang, X. Qian*. A Dual Meta-Learning Framework based on Idle Data for Enhancing Segmentation of Pancreatic Cancer. Medical Image Analysis, v.78, 2022.

3.  X. Chen, Z. Chen, J. Li, Y. Zhang, X. Lin, X. Qian*. Model-driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-transformation. IEEE Transactions on Medical Imaging, 41(1), 2022.

4.  J. Li, C. Feng, Q. Shen, X. Lin, X. Qian*. Pancreatic Cancer Segmentation in Unregistered Multi-parametric MRI with Adversarial Learning and Multi-scale Supervision. Neurocomputing, v.467, 2022.

5.  J. Li, C. Feng, X. Lin, X. Qian*. Utilizing GCN and Meta-Learning Strategy in Unsupervised Domain Adaptation for Pancreatic Cancer Segmentation. IEEE Journal of Biomedical and Health Informatics, 26(1), 2022.

6.  R. Guo, X. Shao, C. Zhang, X. Qian*. Multi-scale Sparse Graph Convolutional Network for the Assessment of Parkinsonian Gait. IEEE Transactions on Multimedia, v.24, 2022.

7.  J. Li, X. Zhu, H. Chen, H. Li, X. Qian*. Pancreas Segmentation with Probabilistic Map Guided Bi-directional Recurrent U-Net. Physics in Medicine and Biology, 66(11), 2021.

8.  X. Song#, M. Mao#X.Qian*. Auto-Metric Graph Neural Network Based on a Meta-learning Strategy for the Diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 25(8), 2021.

(Source code is available at https://github.com/SJTUBME-QianLab/AutoMetricGNN)

9.  H. Li, X. Shao, C. Zhang, X. Qian*. Automated Assessment of Parkinsonian Finger-tapping Tests through a Vision-based Fine-grained Classification Model. Neurocomputing, v.441, 06,2021.

10.  X. Chen, X. Lin, Q. Shen, X. Qian*. Combined Spiral Transformation and Model-driven Multi-modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic CancerIEEE Transactions on Medical Imaging, 40(2), 2021.

(Source code is available at https://github.com/SJTUBME-QianLab/SpiralTransform)

11.  R. Guo, X. Shao, C. Zhang, X. Qian*. Sparse Adaptive Graph Convolutional Network for Leg Agility Assessment in Parkinson’s Disease. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 28(12),2020.

12.  X. Liu, X. Zhou, X. Qian*. Transparency-guided ensemble convolutional neural network for the stratification between pseudoprogression and true progression of glioblastoma multiform in MRI. Journal of Visual Communication and Image Representation, vol.72, 2020

(Source code is available at https://github.com/SJTUBME-QianLab/Transparency-guided-EnsembleCNN)

13.  M. Li, M. D. Chan, X. Zhou, and X. Qian*. DC-Al GAN: Classification of Pseudoprogression and True Tumor Progression of Glioblastoma multiform Based on DCGAN and AlexNet. Medical Physics, 12, 2019.

(Source code is available at https://github.com/SJTUBME-QianLab/DC-AL-GAN)

14.  J. Wang, H. Liu, X. Qian*, et al. Cascaded Hidden Space Feature Mapping, Fuzzy Clustering, and Nonlinear Switching Regression on Large Datasets. IEEE Transactions on Fuzzy Systems, 26(2), 2018.

15.  C. Sun, S. Guo, H. Zhang, J. Li, S. Ma, L. Jin, X. Liu, X. Li*, X. Qian*. Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artificial Intelligence in Medicine, vol.83, 2017.

16.  X. Qian, H. Tan, J. Zhang, et al. Stratification of Pseudoprogression and True Progression of Glioblastoma Multiform Based on Longitudinal Diffusion Tensor Imaging without Segmentation. Medical Physics, 43(11), 2016.

17.  X. Qian, H. Tan, J. Zhang, et al. Objective classification system for sagittal craniosynostosis based on suture segmentation. Medical Physics, 42(9), 2015.

18.  X. Qian, Y. Lin, J. Wang, et al. Segmentation of myocardium from cardiac MR images using a novel dynamic programming based segmentation method. Medical Physics, 42(3), 2015.

19.  X. Qian, J. Wang, S. Guo, et al. An Active Contour Model for Medical Image Segmentation with Application to Brian CT Image. Medical Physics, 2013, 40(2).

教学工作

数据结构(工科平台,大一)

生物医学工程中的数据挖掘 (研究生课程)

软件版权登记及专利

(1) 钱晓华; 李钧; 多模态医学图像分割方法、系统、存储介质及电子设备2020-02-24, 中国, CN2020101124914

(2) 钱晓华; 陈夏晗; 基于多模态的深度学习预测方法、系统、介质及设备2020-02-18, 中国, CN2020100986849

(3) 钱晓华; 陈夏晗; 深度学习中螺旋变换数据扩增方法、系统、介质及设备2020-02-18, 中国, CN202010098682X 

(4) 李强; 庄吓海; 钱晓华; 一种左心室心肌的分割方法和装置, 2019-04-23, 中国, CN104978730B.

荣誉奖励

2019年上海交通大学社会实践优秀指导教师

2019-2020年上海交通大学生物医学工程学院优秀班主任

2021年上海交通大学优秀班主任

联系方式

邮箱地址:xiaohua.qian@sjtu.edu.cn

联系电话:021-62932187

办公地址:徐汇校区教三楼南楼421室