郑健，博导，研究员。主要从事基于人工智能的医学大数据分析、先进射线成像技术等领域的研究工作，研发及产业化相应的成像设备及计算机辅助分析软件。承担国家和地方科技项目多项，包括：国家重点研发计划子课题、国家自然基金、江苏省项目、苏州市科技计划专项等。获评中科院青年创新促进会会员、省部级奖励1次及苏州高新区创新创业领军人才。在IEEE 汇刊（TCYB、TNNLS、TMI、TIP、TBME、JBHI）、European Radiology、CMPB、JMRI、CMIG、Medical Physics、BSPC等国际期刊上发表SCI论文40余篇，申请发明专利30余项，已授权20项。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. 中国科学技术大学科教融合学院优秀导师奖（2022）
2. 中国体视学会智能成像分会委员（2021年-至今）
3. 苏州市医学会医学科技一等奖（2021年，第3完成人）
4. 江苏省科学技术二等奖（2018年，第3完成人）
5. 苏州高新区创新创业领军人才（2017年）
6. 中科院青年创新促进会会员（2014年）
（1）基于医学大数据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可解释性，辅助重大疾病的临床诊断及个性化治疗方案决策。
（2）先进医学成像：利用射线的相位、能谱等信息以及磁纳米粒子的非线性电磁响应特性，开展融合人工智能和物理模型的成像新方法研究，拓展其在肿瘤、心血管等重大疾病诊疗中的应用。
[1] ICL-Net: Global and Local Inter-pixel Correlations Learning Network for Skin Lesion Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022.
[2] 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.
[3] CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration[J]. Computer Methods and Programs in Biomedicine, 2022, 224: 107025.
[4] Pretreatment DCE-MRI-based deep learning outperforms radiomics analysis in predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer[J]. Frontiers in Oncology, 2022, 12: 846775-846775.
[5] FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images[J]. Medical Physics, 2022, 49:144-157.
[6] IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation[J]. Computerized Medical Imaging and Graphics, 2022, 95:102021.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] A radiomics method to classify microcalcification clusters in digital breast tomosynthesis[J]. Medical Physics, 2020, 47(8): 3435-3446.
[12] 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.
[13] 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.
[14] Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network[J]. Biomedical Signal Processing and Control, 2019.
[15] Non-rigid MR-TRUS image registration for image guided prostate biopsy using correlation ratio-based mutual information[J]. BioMedical Engineering OnLine, 16(8): 1-21, 2017.
[16] Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction[J]. BioMedical Engineering OnLine, 16(1), 2017.
[17] Smoothed l0 norm regularization for sparse-view x-ray CT reconstruction[J]. BioMed Research International, Volume 2016, Article ID 2180457.
[18] Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares[J]. BioMedical Engineering OnLine, 15(1), 2016.
[19] A prior-based metal artifact reduction algorithm for X-ray CT[J]. Journal of X-ray Science and Technology, 2015, 23(2): 229-241.
郑健 研究员
研究方向：
电子邮件：zhengj@sibet.ac.cn
电 话：0512-69588115
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