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。承担国自然面上、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。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。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60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.      济南市产业领军人才（2022）

3.      中国体视学会智能成像分会委员（2021年-至今）

4.      苏州市医学会医学科技一等奖（2021年，第3完成人）

5.      江苏省科学技术二等奖（2018年，第3完成人）

6.      苏州高新区创新创业领军人才（2017年）

7.      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 (Accetped)

[2]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.

[3]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

[4]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.

[5]A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising[J]. Computerized Medical Imaging and Graphics, 2023, 107: 102237.

[6]Multimodal Cross Enhanced Fusion Network for Diagnosis of Alzheimer's Disease and Subjective Memory Complaints[J]. Computers in Biology and Medicine, 2023: 106788.

[7]ICL-Net: Global and Local Inter-pixel Correlations Learning Network for Skin Lesion Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022.

[8]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.

[9]CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration[J]. Computer Methods and Programs in Biomedicine, 2022, 224: 107025.

[10]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.

[11]FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images[J]. Medical Physics, 2022, 49:144-157.

[12]IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation[J]. Computerized Medical Imaging and Graphics, 2022, 95:102021.

[13]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.

[14]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.

[15]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.

[16]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.

[17]A radiomics method to classify microcalcification clusters in digital breast tomosynthesis[J]. Medical Physics, 2020, 47(8): 3435-3446.

[18] 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.

[19]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.

[20] Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network[J]. Biomedical Signal Processing and Control, 2019.