Lingxiao Zhao graduated from Dept. of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, with a Bachelor’s degree in 2000. He worked as a M.Sc. student and then a Ph.D. researcher in the Computer Graphics and Visualization group of the Delft University of Technology (TU Delft), the Netherlands, from 2002 to 2008. His research topic during this period was a part of the advanced development of medical imaging and visualization project founded by Philips, and he mainly conducted his daily work at the base site of Philips Healthcare, Best, the Netherlands. Since 2009 after he received his Ph.D. degree, Lingxiao Zhao worked in Philips Healthcare and Philips Research HTC for six and a half years as a full-time employee. In August 2015, he started his new position in Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences (CAS), Suzhou, China. He is currently working as the director of the Healthcare Information Technology Innovation Center of SIBET. He is also a guest professor and doctoral advisor of China University of Science and Tehcnology.

The current research effort of Lingxiao Zhao mainly focuses on medical image processing and AI-assisted clinical diagnosis and therapy, medical visualization and virtual reality in surgery simulation and guidance, software engineering for medical devices and applications. In recent years, he and his team have participated or presided over a couple of research and development (R&D) projects, including 3 key R&D projects administrated by Ministry of Science and Technology China, 1 CAS scientific research equipment development project, 2 key R&D projects of Jiangsu Province, 2 key R&D projects of cities of Suzhou and Changzhou respectively, and in total more than 10 joint R&D projects in cooperation with enterprises and incubating projects of SIBET.

Lingxiao Zhao is leading an R&D team of more than 30 members. In recent five years, he and his M.Sc. and Ph.D. students published more than 20 academic papers on international scientific research journals and symposiums, including IEEE TVCG, CGF, MBEC, CBM, IEEE VR and IEEE ISMAR, etc. His R&D team has also applied for patents (3 authorized) and obtained 14 authorized software copyrights. Lingxiao Zhao and his team have carried out in-depth research and engineering development work in the fields of AI-assisted diagnosis of brain tumors and breast cancer using MRI, AI-assisted screening for early digestive tract cancer using multi-modality endoscopic images and cervical cancer cells using digital pathological images, as well as clinical surgery simulation and guidance using augmented reality/mixed reality techniques.

1.       Medical Image Processing and AI-assisted Diagnosis: Research on how to use computer software technologies and methods, such as digital image processing and deep learning, to assist clinicians in achieving more accurate and efficient diagnosis and treatment of diseases in the human digestive system, brain, breast, respiratory system and other parts of the body.

2.       Medical Visualization and Virtual Reality in Surgery Simulation and Guidance: Regarding the requirements in clinical surgical scenarios, research on augmented/mixed reality methods for surgery simulation, planning and navigation, study and develop key techniques about 3D rendering, visualization, 3D modeling and geometric analysis, displaying, user interaction and etc., using medical image data, and explore innovative ways to assist surgeons in conducting efficient and precise surgical operations.

3.       Big Data and Cloud Computing in Healthcare: Research for new principles, methods and techniques that can be used to analyze and process multi-model healthcare data, design novel big data algorithms for early warning, tracking and tracing of critical illness and infectious diseases, develop relevant SaaS software systems on web-based cloud computing platform.

4.       Software Engineering for Medical Devices and Clinical Applications: study methods and techniques, such as scheduling and controlling of hardware components, data acquisition and processing, high speed data transmission and quality control, for various high-end medical and scientific instruments, use software engineering technology for the software architectural design and function implementation of large-scale industrial software systems.

1.       Third-level talented scientist of the “333 High-level Talent Training Program” of Jiangsu Province

2.       High-level Tight Talent for Colleges and Research Institutes of Suzhou

[1] Zhao L, Botha C, Bescos J, Truyen R, Vos F, Post FH. Lines of curvature for polyp detection in virtual colonoscopy[J]. IEEE Transactions on Visualization and Computer Graphics, 2006, 12(5): 885-892. DOI:10.1109/TVCG.2006.158.

[2] Cheng H, Xu C, Wang J, Chen Z, Zhao L. Fast and Accurate Illumination Estimation Using LDR Panoramic Images for Realistic Rendering[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(12): 5235-5249. DOI: 10.1109/TVCG.2022.3205614.

[3] Cheng H, Xu C, Wang J, Zhao L. Quad-fisheye Image Stitching for Monoscopic Panorama Reconstruction[J]. Computer Graphics Forum. 2022, 41(6): 94-109. DOI: https://doi.org/10.1111/cgf.14512.

[4] Fan H, Luo D, Zhu J, Schalek R.l., Chen Z, Zhao L, Zhou R, Sun M, Shi G, Xie Y, Lichtman J.W., Zhang R. Optical Multilayer Interference Tomography Compatible with Tape-Based Serial SEM for Mesoscale Neuroanatomy[J]. ACS Photonics, 2022, 9(1): 25-33. DOI:10.1021/acsphotonics.1c00892.

[5] Ma X, Zhao Y, Lu Y, Li P, Li X, Mei N, Wang J, Geng D, Zhao L, Yin B. A Dual-branch Hybrid Dilated CNN Model for the AI-assisted Segmentation of Meningiomas in MR Images[J]. Computers in Biology and Medicine, 2022, 151(Pt A): 106279. DOI: 10.1016/j.compbiomed.2022.106279.

[6] Ma X, Zhao L, Dang S, Zhao Y, Lu Y, Li X, Li P, Chen Y, Mei N, Geng D. Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas[J]. Journal of Computer Assisted Tomography, 2022: 10.1097. DOI:

10.1097/RCT.0000000000001565.

[7] Wang J, Long Q, Liang Y, Song J, Feng Y, Li P, Sun W, Zhao L. AI-assisted Identification of Intrapapillary Capillary Loops in Magnification Endoscopy for Diagnosing Early-stage Esophageal Squamous Cell Carcinoma: a Preliminary Study[J]. Medical & Biological Engineering & Computing, 2023, 61(7): 1631-1648. DOI: 10.1007/s11517-023-02777-3.

[8] Feng Y, Liang Y, Li P, Long Q, Song J, Li M, Wang X, Cheng CE, Zhao K, Ma J, Zhao L. Artificial Intelligence Assisted Detection of Superficial Esophageal Squamous Cell Carcinoma in White-light Endoscopic Images by Using a Generalized System[J]. Discover Oncology, 2023, 14(1): 73. DOI: 10.1007/s12672-023-00694-3.

[9] Sun W, Li P, Liang Y, Feng Y, Zhao L. Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images[J]. Bioengineering (Basel), 2023, 10(11): 1288. DOI: 10.3390/bioengineering10111288.

[10] Zhang J, Zhao Y, Lu Y, Li P, Dang S, Yin B, Zhao L. Meningioma Consistency Assessment Based on the Fusion of Deep Learning Features and Radiomics Features[J]. European Journal of Radio-logy, 2024, 170: 111250. DOI: 10.1016/j.ejrad.2023.111250.

[11] Cheng H, Xu C, Chen Z, Wang J, Chen Y, Zhao L. A Mixed Reality Framework for Interactive Realistic Volume Rendering with Dynamic Environment Illumination[C]. Proceedings of IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (IEEE VR), 2023, pp: 741-742. DOI: 10.1109/VRW58643.2023.00213.

[12] Cheng H, Xu C, Chen X, Chen Z, Wang J, Zhao L. Realistic Volume Rendering with Environ-ment-Synced Illumination in Mixed Reality[C]. IEEE International Symposium on Mixed and Augmented Reality Adjunct (IEEE ISMAR-Adjunct), 2023, pp: 423-428. DOI: 10.1109/ISMAR-Adjunct60411.2023.00090.

[13] R.S. Laramee, H. Hauser, L. Zhao (co-first author), and F.H. Post. Topology-based Flow Visualization, the State of the Art. In Topology-Based Methods in Visualization (H. Hauser, H. Hagen, and H. Theisel, eds.), pp. 1-20, Springer Verlag, Mathematics and Visualization Series, 2007 (ISBN 978-3-540- 70822-3).

[14] L. Zhao, C.P. Botha, R. Truyen, and F.H. Post. Efficient Seeding and Defragmentation of Curvature Streamlines for Colonic Polyp Detection[C]. In Proc. of SPIE Medical Imaging 2008: Physiology, Function, and Structure from Medical Images, vol.6916, Xiaoping P. Hu, Anne V. Clough, Editors, 69160E, March 12, 2008.

[15] L. Zhao, V.F. van Ravesteijn, C.P. Botha, R. Truyen, F.M. Vos and F.H. Post. Surface Curvature Line Clustering for Polyp Detection in CT Colonography[C]. In Proc. of the First Eurographics Workshop on Visual Computing for Biomedicine (VCBM'08), pp. 53-60, Oct, 2008 (期刊封面论文).

[16] V.F. van Ravesteijn, L. Zhao (co-first author), C.P. Botha, F.H. Post, F.M. Vos and L.J. van Vliet. Combining Mesh Volume and Streamline Representations for Polyp Detection in CT Colonography[C]. In Proc. of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'09), pp. 907-910, June-July Boston, 2009.