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https://cs.nankai.edu.cn/info/1240/3356.htm
马玲，于2011年7月和2017年6月获得吉林大学工学硕士与北京理工大学工学博士学位（计算机应用技术专业）。攻读博士学位期间，曾赴美国埃默里大学（Emory University）生物医学工程专业学习两年。主要研究方向为医学影像分析，包括 CT 影像、超声影像、MRI 影像、以及高光谱影像中，器官的分割、病灶的定位与检测、疾病的识别与检索等。以一作或通讯发表论文十余篇。主持国家自然科学基金青年基金项目1项，中国博士后科学基金面上项目1项，中国科学技术协会高端科技创新智库青年项目1项，横向项目1项，参与国家重点研发计划项目1项及国自然面上项目3项，CCF人工智能学会会员。
主持项目：
1. 基于多信息深度融合的肺部GGO结节自动检测方法研究，国家自然科学基金青年项目，2020.01-2022.12
2. 基于CT影像征象的肺部疾病计算机辅助诊断方法研究，中国博士后科学基金，2019.01-2020.12
3.人工智能的伦理与治理规则研究，中国科学技术协会高端科技创新智库青年项目，2020.07-2020.12
参与项目：
1. 头颈部动脉斑块负荷及性质影响脑组织代谢及循环机制的多模态MRI评估，国家自然科学基金面上项目，2019.01-2022.12
2. 基于深度学习的小物体检测及其异构计算技术研究，国家自然科学基金面上项目，2019.01-2022.12
3. 基于主动轮廓模型的图像分割与目标跟踪研究，国家自然科学基金面上项目，2019.01-2022.12
4. 面向影像表现的肺部CT图像检索方法研究，国家自然科学基金面上项目，2012.01-2015.12
发表论文：
1. Ling Ma, et al. A multi-level similaritymeasure for the retrieval of the common CT imaging signs of lung diseases. MedBiol Eng Comput 58, 1015–1029 (2020).
2. Ling Ma, et al. Adaptive deep learningfor head and neck cancer detection using hyperspectral imaging. Vis. Comput. Ind.Biomed. Art 2, 18 (2019).
3. Ling Ma, et al. A new method of contentbased medical image retrieval and its applications to CT imaging sign retrieval[J].Journal of Biomedical Informatics, 2017, 66: 148–158.
4. Ling Ma, et al. Learning with Distributionof Optimized Features for Recognizing Common CT Imaging Signs of Lung Diseases[J]. Physics in Medicine and Biologys, 2017, 62(2): 612-632.
5. Ling Ma, et al. A new classifier fusionmethod based on historical and on-line classification reliability forrecognizing common CT imaging signs of lung diseases [J]. Computerized MedicalImaging and Graphics, 2015, 40: 39-48.
6. Xiabi Liu, Ling Ma, et al. Recognizingcommon CT imaging signs of lung diseases through a new feature selection methodbased on Fisher criterion and genetic optimization [J]. Biomedical and HealthInformatics, IEEE Journal of, 2015, 19(2): 635-647.
7. Ling Ma, et al. A Random Walk basedProstate Segmentation Algorithm from 3D Ultrasound Images [J]. Medical Physics.2017.
8. Ling Ma, et al.. Combination Learningfor Prostate Segmentation on 3D CT Images [J]. Medical Physics. 2017..
10. Ling Ma, et al. Automatic segmentationof the prostate on CT images using deep learning and multi-atlas fusion [C]. InSPIE Medical Imaging, 2017: 101332O-101332O.
11. Ling Ma, et al. Deep Learning basedClassification for Head and Neck Cancer Detection with Hyperspectral Imaging inan Animal Model [C]. In SPIE Medical Imaging, 2017: 101372G-101372G.
12. Ling Ma, et al. Combining populationand patient-specific characteristics for prostate segmentation on 3D CT images[C]. In SPIE Medical Imaging, 2016: 978427-978427.
13. Ling Ma, et al. Random walk basedsegmentation for the prostate on 3D transrectal ultrasound images [C]. In SPIEMedical Imaging, 2016: 978607-978607.
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