钱大宏 - 上海交通大学 - 生物医学工程学院

个人简介

钱大宏,长聘教授，博士生导师。在国内外的高科技公司有十多年的研发经历。目前从事的研究方向是人工智能软件和硬件在医疗方面广泛的应用。教育背景博士学位，哈佛大学；硕士学位，德克萨斯大学奥斯汀分校；学士学位，浙江大学；工作经历学术工作经验： 2017 -- 至今上海交通大学，生物医学工程学院教授，医疗机器人研究院智能人机交互中心主任； 2013 – 2017 浙江大学，转化医学院，教授教学工作本科生课程《智能医疗与创新》；研究生课程《人工智能与医学》；大学生科创工作室。

研究领域

多模态多中心人工智能辅助诊疗；内镜医疗机器人的算法、器件与系统；人工智能可穿戴系统。

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

1. Y. Tian, J. Wang, W. Yang, J. Wang, D. Qian, “Deep Multi-Instance Transfer Learning for Pneumothorax Classification in Chest X-ray Images,” Medical Physics，2021. 2. J. Xu, J. Wang, X. Bian, J. Zhu, C. Tie, X. Liu, Z. Zhou, X. Ni, D. Qian, “Deep learning for nasopharyngeal carcinoma identification using both white light and narrow-band imaging endoscopy,” The Laryngoscope, vol 0, pp. 1-9, 2021. 3. R. Li, Y. Huang, H. Chen, X. Liu, Y. Yu, D. Qian, and L. Wang, “3D Graph-Connectivity Constrained Network for Hepatic Vessel Segmentation”, IEEE Journal of Biomedical and Health Informatics, 2021. 4. J. Wang, C. Yuan, C. Han, Y. Wen, H. Lu, C. Liu, Y. She, J. Deng, B. Li, D. Qian, C. Chen., “IMAL-Net: Interpretable Multi-task Attention Learning Network for Invasive Lung Adenocarcinoma Screening in CT Images,” Medical Physics，October 21, 2021. 5. Y. Bao, J. Wang, T. Li, L. Wang, J. Xu, J. Ye and D. Qian, “Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images”, MICCAI Workshop on Ophthalmic Medical Image Analysis, Strasbourg, France, September 27, 2021. 6. Z. Zuo, P. Wang, X. Chen, L. Tian, H. Ge, D. Qian, “SWnet: A deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures”, BMC Bioinformatics, vol 22, pp. 434, 2021. 7. J. Wang, C. Liu, J. Li, C. Yuan, L. Zhang, C. Jin, J. Xu, Y. Wang, Y. Wen, H. Lu, B. Li, C. Chen, X. Li, D. Shen, D. Qian and J. Wang, “y-time prediction of COVID-19 patients”, NPJ Digital Medicine, vol 4, pp. 124, August 16, 2021. 8. J. Zhang, W. Hu, S. Li, Y. Wen, Y. Bao, H. Huang, C. Xu, D. Qian, “Chromosome Classification and Straightening Based on an Interleaved and Multi-task Network”, IEEE Journal of Biomedical and Health Informatics, vol 25, pp. 3240-3251, August 2021. 9. C. Yuan, M. Zhang, X. Huang, W. Xie, X. Lin, W. Zhao, B. Li, D. Qian, “Diffuse Large B-cell Lymphoma Segmentation in PET-CT Images via Hybrid Learning for Feature Fusion”, Medical Physics, vol. 48, pp. 28, March, 2021. 10. J. Xu, R. Zhao, Y. Yu, Q. Zhang, X. Bian, J. Wang, Z. Ge, D. Qian, “Real-Time Automatic Polyp Detection in Colonoscopy using Feature Enhancement Module and Spatiotemporal Similarity Correlation Unit”, Biomedical Signal Processing and Control, vol. 66, February 9, 2021. 11. D. Chen, J. Zhang, D. Qian, D. Chen, K. Wang, X. Dong, “Segmentation of Lung Adenocarcinoma Cells’ Pathological Image Based on Deep Learning Method”, Proceedings of the 2021 4th International Conference on Image and Graphics Processing (ICIGP 2021), Sanya, China, January 1-3, 2021. 12. D. Liu, X. Peng, X. Liu, Y. Li, Y. Bao, J. Xu, X. Bian, W. Xue, D. Qian, “A Real-Time System Using Deep Learning to Detect and Track Ureteral Orifices During Urinary Endoscopy”, Computers in Biology and Medicine, vol 128, January 2021. 13. J. Wang, Y. Bao, Y. Wen, H. Lu, H. Luo, Y. Xiang, X. Li, C. Liu, D. Qian, “Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images”, IEEE Transactions on Medical Imaging, pp. 0, November 2020. 14. X. Guan, S. Wang, P. Kuang, H. Lu, M. Zhang, D. Qian, X. Xu, “The usefulness of imaging quantification in discriminating non-calcified pulmonary hamartoma from adenocarcinoma”, Frontiers in Oncology, vol. 10, October 22, 2020. 15. M. Zhang, Y. Bao, W. Rui, C. Shangguan, J. Liu, J. Xu, X. Lin, M. Zhang, X. Huang, Y. Zhou, Q. Qu, H. Meng, D. Qian, B. Li, “Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients with Non-Small Cell Lung Cancer”, Frontiers in Oncology, vol 10, October 8, 2020. 16. C. Yuan, Y. Tang, D. Qian, “Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution”, MICCAI 2020, Lima, Peru, October 4-8, 2020. 17. R. Zhang, G. Li, Z. Li, S. Cui, D. Qian, Y. Yu, “Adaptive Context Selection for Polyp Segmentation”, MICCAI 2020, Lima, Peru, pp. 253-262, September 29, 2020. 18. L. Tong, C. Ning, Y. Wen, X. Xu, C. Ye, S. Zhang, D. Qian, Y. Liang, “Differentiation of primary open angle-closure glaucoma and primary open angle glaucoma based on disc image with a deep learning method”, Association for Research in Vision and Ophthalmology (ARVO 2020), Baltimore, MD, US, May 3-7, 2020. 19. J. Wang, X. Chen, H. Lu, L. Zhang, J. Pan, Y. Bao, J. Su, D. Qian, “Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary sub-solid nodules in CT images”, Medical Physics, vol. 47, No. 4, pp1738-1749, February 5, 2020. 20. Z. Zhang, K. Lin, Z. Zuo, D. Qian, D. Huang, J. Li, “Prediction for atrial fibrillation recurrence after catheter ablation using an artificial intelligence-assisted coronary sinus electrogram”, American Heart Association Scientific Sessions (AHA2019), Philadelphia, PA, November 16-18, 2019. 21. Z. Cheng, J. Zhang, N. He, Y. Li, Y. Wen, H. Xu, R. Tang, Z. Jin, E. Mark Haacke, F. Yan, D. Qian, “Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson’s Disease”, Frontiers in Aging Neuroscience, vol. 11, pp. 167, July 16, 2019. 22. L. Lou, L. Yang, X. Ye, Y. Zhu, S. Wang, L. Sun, D. Qian, J. Ye, “A Novel Approach for Automated Eyelid Measurements in Blepharoptosis Using Digital Image Analysis”, Current Eye Research, vol.44, No. 10, pp. 1075-1079, May 31, 2019. 23. S. Wang, H. Zhang, D. Qian, “A Semi-supervised Bleeding Detection Method in Wireless Capsule Endoscopy”, Digestive Disease Week (DDW 2019), San Diego, CA, USA, May 18-21, 2019. 24. L. Zhou, K. Wang, H. Sun, S. Zhao, X. Chen, D. Qian, H. Mao, J. Zhao, “Novel Graphene Biosensor Based on the Functionalization of Multifunctional Nano-bovine Serum Albumin for the Highly Sensitive Detection of Cancer Biomarkers”, Nano-Micro Letters, vol.250, pp. 13, February 20, 2019. 25. Z. Zuo, K. Wang, L. Gao, V. Ho, H. Mao, D. Qian, “A novel mass-producible capacitive sensor with fully symmetric 3D structure and microfluidics for cell detection”, Sensors, vol.19, pp. 325, January 15, 2019. 26. M. Zhou, K. Jin, S. Wang, J. Ye, D. Qian, “Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment”, IEEE Transactions on Biomedical Engineering, vol. 65, No. 3, pp: 521-527, March 2018. 27. X. Ye, S. Wang, Y. Zhu, H. Shao, L. Lou, D. Qian, J. Ye, “Automatic Design and Fabrication of a Custom Ocular Prosthesis using 3D Volume Difference Reconstruction (VDR)”, IEEE Access, vol. 6, No. 1, pp. 14339~14346, February 5, 2018. 28. K. Jin, M. Zhou, S. Wang, L. Lou, Y. Xu, J. Ye, D. Qian, “Computer-aided diagnosis based on enhancement of degraded fundus photographs”, Acta Ophthalmology, vol.96, No. 3, pp. 320-326, November 1, 2017. 29. K. Jin, H. Lu, Z. Su, C. Cheng, J. Ye, D. Qian, “Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera”, BMC Ophthalmology, vol. 17, No. 1, pp.89, June 13, 2017. 30. S. Wang, K. Jin, H. Lu, C. Cheng, J. Ye, D. Qian, “Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs”, IEEE Transactions on Medical Imaging, vol.35, No. 4, pp.1046-1055, April 2016. 31. C. Deng, Y. Sheng, S. Wang, W. Hu, S. Diao, D. Qian, "A CMOS Smart Temperature Sensor With Single-Point Calibration Method for Clinical Use”, IEEE Transactions on Circuits and Systems II-Express Briefs, vol.63, No.2, pp136-40, Feb. 2016.

学术兼职

上海交通大学医学院附属瑞金医院核医学科“广慈”双聘教授