代表性著作:
1.An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland. Eur Radiol. 2024 Apr 3. doi: 10.1007/s00330-024-10719-2(中科院分区:2区,JCR分区:Q1,IF:5.9)
2. Deep learning based de-overlapping correction of projections from a flat-panel microarray X-ray source: Simulation study. Physica medica. 2023 Jul;111:102607. doi: 10.1016/j.ejmp.2023.102607. Epub 2023 May 19. PMID: 37210964. ( 中科院分区:3区,JCR分区:Q2,IF:3.4)
3. Bone suppression of lateral chest X-rays with imperfect and limited dual-energy subtraction images. Computerized Medical Imaging and Graphics. 2023 Apr;105(2023)102186.(中科院分区:工程2区,JCR分区:Q1,IF:5.7)
4.Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke. Frontiers in Neurology. 2022 Sep 28;13:982783. ( 中科院分区:3区,JCR分区:Q2,IF:3.4)
5.Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study. Frontiers in Oncology. 2022 Sep 16;12:916126. (中科院分区:3区,JCR分区:Q2,IF: 4.7)
6.Lesion-specific exposure parameters for breast cancer diagnosis on digital breast tomosynthesis and full-field digital mammography[J]. Biomedical Signal Processing and Control, 2022, 77: 103752.( 中科院分区:2区,JCR分区:Q2,IF: 5.1)
7.Can a computer-aided mass diagnosis model based on perceptive features learned from quantitative mammography radiology report improve junior radiologists’ diagnosis performance? An observer study. Frontiers in Oncology. 2021 Dec 17;11:773389. (中科院分区:3区,JCR分区:Q2,IF: 4.7)
8.A System Pharmacology Model for Decoding the Synergistic Mechanisms of Compound Kushen Injection in Treating Breast Cancer. Frontiers in Pharmacology. 12:723147. doi: 10.3389/fphar.2021.723147(中科院分区:2区,JCR分区:Q1,IF: 5.6)
9.The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT. Science progress, 104(3), 368504211016204. (中科院分区:4区,JCR分区:Q3,IF: 2.1)
10.Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms. European Radiology, 2022 Mar;32(3):1652-1662. doi:10.1007/s00330-021-08271-4. (中科院分区:2区,JCR分区:Q1,IF:7.034)
11.A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors. European radiology, 2022 Feb;32(2):1371-1383. (中科院分区:2区,JCR分区:Q1,IF:7.034)
12.Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging. Quantitative Imaging in Medicine and Surgery, 2021;11(8):3684-3697. (中科院分区:2区,JCR分区:Q2,IF:4.630/2.8)
13.Can the delayed phase of quantitative contrast-enhanced mammography improve the diagnostic performance on breast masses? Quantitative Imaging in Medicine and Surgery, 2021;11(8):3684-3697. (中科院分区:2区,JCR分区:Q2,IF:4.630)
14.Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors. BioMed Research International, vol. 2021, Article ID 8811056, 10 pages, 2021. https://doi.org/10.1155/2021/8811056. (中科院分区:3区,JCR分区:Q3,IF:3.246)
15.An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19. BioMed Research International, vol. 2021, Article ID 8840835, 9 pages, 2021. https://doi.org/10.1155/2021/8840835(中科院分区:3区,JCR分区:Q3,IF:3.246)
16.临床、CT影像组学及融合模型预测肝细胞癌分化程度[J]. 中国医学影像技术,2021,37(7):1029-1033.
17.基于2015版尘肺病诊断标准影像报告及诊断分期的一致性研究[J].实用医学杂志,2021,37(6):797-801.
18.腮腺肿物超声图像特征、临床特征及恶性风险预测模型 [J] . 中华超声影像学杂志, 2021, 30(7) : 609-614. DOI: 10.3760/cma.j.cn131148-20210120-00048.
19.Association of the Differences in Average Glandular Dose with Breast Cancer Risk [J]. BioMed Research International, 2020, 2020(8943659). (中科院分区:3区,JCR分区:Q3,IF:3.246)
20.Additive value of exposure parameters for breast cancer diagnosis in digital mammography [J]. European radiology, 2021 Apr;31(4):2539-2547. (中科院分区:2区,JCR分区:Q1,IF:7.034)
21.OXPHOS-dependent metabolic reprogramming prompts metastatic potential of breast cancer cells under osteogenic differentiation. British journal of cancer, 2020 Nov;123(11):1644-1655(中科院分区:1区,JCR分区:Q1,IF:9.075/8.8)
22.X线与CT在新型冠状病毒肺炎诊断中的应用[J].广东医学,2020,41(07):656-660.
23.人工智能技术在肝纤维化定量评价的研究进展[J].国际医学放射学杂志,2020,05:578-581
24.人工智能在功能与分子影像学的研究进展[J].分子影像学杂志,2020,43(01):1-6.
25.颅面骨骨肉瘤的CT和MRI诊断及鉴别,中国医学计算机成像杂志,2019,25:53-57
26.Extraskeletal Myxoid Chondrosarcoma: A Comparative Study of Imaging and Pathology, BioMed Research International, 2018, 2018:1-9.(中科院分区:3区,JCR分区:Q3,IF:3.246)
27.误诊脊柱骨母细胞瘤的影像征象分析,实用放射学,2018,34(04)
28. E74‑like ETS transcription factor 5 facilitates cell proliferation through regulating the expression of adenomatous polyposis coli 2 in non‑small cell lung cancer. International journal of molecular medicine, 2023. September, 52(3), 75. https://doi.org/10.3892/ijmm.2023.5278 (中科院分区:3区,JCR分区:Q2,IF:5.4)
29.Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification, European Radiology,2020;30(2):778–788.(中科院分区:2区,JCR分区:Q1,IF:7.034)
30.COVID-19疫情下影像科室的应急管理策略[J].分子影像学杂志,2020,43(02):278-281.
31.Matching Corresponding Regions of Interest on Cranio-caudal and Medio-lateral Oblique View Mammograms, IEEE Access,2019.7:p.31586-31597.(中科院分区:3区,JCR分区:Q2,IF:3.476/3.9)
32.The mammography and MRI manifestations of adenomyoepithelioma of the breast, Clinical Radiology, 2016, 71:235-243.(中科院分区:4区,JCR分区:Q2,IF:3.389)
33.Cathepsin L is involved in proliferation and invasion of breast cancer cells, Neoplasma, 2016, 63(1):30-36.(中科院分区:4区,JCR分区:Q3,IF:3.409)
34.基于纤维束的空间统计方式的复发缓解型多发性硬化患者磁共振扩散张量成像与正常人对照, 中国医学物理学杂志, 2016, 33(8):799-804
35.Cu滤波器对不同胸部X射线成像方式的辐射剂量影响, 现代医院, 2016, 16(7):993-994
36.颈椎结核16例影像学表现与分析, 陕西医学杂志, 2014, 43(7):793-796
37.优化数字乳腺断层摄影曝光条件的体模显像, 中国介入影像与治疗学, 2014, 11(12): 813-817
科研项目:
主持:
1.国家重点研发计划课题《基于第三方服务模式的乳腺癌筛查方案优化及应用示范》
2.广东省自然科学基金项目《多组学联合分析Her-2阳性乳腺癌微钙化形成机制及Her-2影像学标志物》
3.广东省自然科学基金项目《乳腺癌转移病灶的人工智能识别模型初步构建及临床应用》
4.广东省医学科学技术研究基金《基于医学影像大数据及卷积神经网络的 X线胸片虚拟双能减影方法及应用研究》
5.赣州市科技计划项目《以医院为基础的新发重大传染病预警、应对和运营优化》
6.南方医科大学科研启动计划项目 《基于SD大鼠乳腺癌模型的多模态影像特征预测分子亚型及分子靶向治疗疗效研究》
参与:
1.国家自然科学基金 《基于任务驱动的光子散射快速蒙特卡罗模拟研究》
2.广东科技计划项目 《X射线超声双模态乳腺三维成像系统的研发及产业化》
3.广东科技计划项目《数字乳腺层析成像系统关键技术研发及整机研制》
4.广州科技计划项目《乳腺断层成像系统研制与临床应用研究》
5.南方医科大学临床研究启动计划项目 《乳腺多模态影像学在早期乳腺癌筛查及诊断中的临床研究》
平台:
广东省“乳腺影像智能诊断”生物医学创新平台 (培育B类)
广州市医用放射成像与检测技术重点实验室
专利成果:
1.发明专利,ZL202110788291.5,一种融合影像和临床特征信息的乳腺癌风险预测系统
2.发明专利,ZL201810663925.2,一种基于红外摄像头的X线辅助摄影方法、系统及装置
3.实用新型专利,CN213875484U,一种组织标本X线辅助摄影装置
4.发明专利,CN111772653A一种钡剂吞咽的识别曝光方法、系统、装置及介质
5.发明专利,201810582349.9,一种基于卷积神经网络的乳腺密度分类方法、系统及装置
6.发明专利,CN107714070B,基于数字化断层融合图的乳腺病灶定位方法、系统和装置
7.实用新型专利,201721153936.3,一种颈椎支撑装置及颈椎动力侧位的X线摄影系统
8.发明专利,CN201611216579.0,一种X线断层融合的辐射剂量相关参数的测定方法
9.发明专利,CN105447866A,基于卷积神经网络的X线胸片骨抑制处理方法
10.实用新型专利,CN203829012U,乳腺穿刺定位压迫器
11.PCT国际发明专利,基于卷积神经网络的X线胸片骨抑制处理方法
12. 发明专利,CN112381772B,一种基于深度卷积神经网络的侧位胸片骨抑制方法
13.发明专利,CN108392215B,一种同侧异位乳腺钼靶图像的点位置关联方法
14.实用新型专利,CN208228951U,一种全脊柱拼接侧位的X线辅助摄影架