郑健，博士生导师，研究员。主要从事基于人工智能的医学影像技术研究。承担JKW重点课题、国家自然基金面上项目、国自然联合基金课题、山东省重大项目、江苏省自然基金等多项国家和省级科研项目。截至目前，已在IEEE 汇刊（TCYB、TNNLS、TMI、TIP、TBME、JBHI）、CVPR、MICCAI、European Radiology、CMPB、JMRI、CMIG、Medical Physics、BSPC等高水平国际期刊及会议上发表SCI检索论文60余篇，申请发明专利30余项，已授权20项。基于上述研究成果，获江苏省科学技术二等奖一次，入选中科院青年创新促进会会员、济南市产业领军人才和苏州高新区双创人才。指导学生曾获“国家奖学金”、“中科院南京分院院长优秀奖学金”、“伍宜孙奖学金”等荣誉。
1. 中国神经科学学会麻醉与脑功能分会委员（2024年-至今）
2. 江苏医学科技三等奖（2023年，第2完成人）
3. 中国科学技术大学科教融合学院优秀导师奖（2022）
4. 济南市产业领军人才（2022）
5. 中国体视学学会智能成像分会委员（2021年-至今）
6. 江苏省科学技术二等奖（2018年，第3完成人）
7. 苏州高新区创新创业领军人才（2017年）
8. 中科院青年创新促进会会员（2014年）
（1）基于医学人工智能的疾病辅助诊断及决策：利用人工智能技术分析影像、病理、基因、分子等多源数据中的深层定量特征及关联模型，辅助重大疾病的临床诊断及个性化治疗方案决策。
（2）先进成像技术：基于X射线的吸收、相变及能谱等多特性以及磁纳米粒子的非线性电磁响应，开展融合人工智能和物理模型的成像新方法研究，推动肿瘤、心血管、脑疾病等重大疾病的机制研究及诊疗技术进展。
[1]SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis[J]. IEEE Transactions on Cybernetics, 2024.
[2]Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.
[3]NeighborNet: Learning Intra-and Inter-Image Pixel Neighbor Representation for Breast Lesion Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2024.
[4]Low-dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network with Noise Encoding Transfer Learning[J]. IEEE Transactions on Medical Imaging, 2023.
[5]Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023
[6]CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis[J]. Computer Methods and Programs in Biomedicine, 2023, 242: 107831.
[7]A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising[J]. Computerized Medical Imaging and Graphics, 2023, 107: 102237.
[8]Multimodal Cross Enhanced Fusion Network for Diagnosis of Alzheimer's Disease and Subjective Memory Complaints[J]. Computers in Biology and Medicine, 2023: 106788.
[9]ICL-Net: Global and Local Inter-pixel Correlations Learning Network for Skin Lesion Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022.
[10]CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising[J]. Computers in Biology and Medicine, 2022, 147: 105759.
[11]CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration[J]. Computer Methods and Programs in Biomedicine, 2022, 224: 107025.
[12]FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images[J]. Medical Physics, 2022, 49:144-157.
[13]IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation[J]. Computerized Medical Imaging and Graphics, 2022, 95:102021.
[14]3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25 (3):764-773.
[15]Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer[J]. European Radiology, 2021: 1-16.
[16]Locally adaptive total p-variation regularization for non-rigid image registration with sliding motion[J]. IEEE Transactions on Biomedical Engineering，2020, 67(9): 2560-2571.
[17]Unsupervised learning for deformable registration of thoracic CT and cone-beam CT based on multiscale features matching with spatially adaptive weighting[J]. Medical Physics, 2020, 47(11): 5632-5647.
[18]A radiomics method to classify microcalcification clusters in digital breast tomosynthesis[J]. Medical Physics, 2020, 47(8): 3435-3446.
[19] Non-rigid image registration using spatially region-weighted correlation ratio and GPU-acceleration[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(2): 766-778.
[20]Multi-domain features for reducing false positives in automated detection of clustered microcalcifications in digital breast tomosynthesis[J]. Medical Physics, 2019, 46(3): 1300-1308.
[21] Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network[J]. Biomedical Signal Processing and Control, 2019.
郑健 研究员
研究方向：
电子邮件：zhengj@sibet.ac.cn
电 话：0512-69588115
通讯地址：