Prof. Dr. Xiaoyi Jiang
Biomedical image/pattern analysis and understanding/ pattern recognition/ machine learning
Computer Sciences
Imaging Technology
Mathematics / Math. Modeling / Image Analysis
My research group works in the broad fields of computer vision, pattern recognition, and machine learning. One research focus is biomedical image and pattern analysis. Analysis of images and videos is of essential importance to basic and applied research in biology and medicine. Fundamental problems of biomedical image analysis include image processing, multimodal and temporal image registration, image segmentation, shape analysis, motion analysis, and multiscale analysis. In addition to extracting useful image features, we also work on applying pattern recognition and machine learning, in particular deep learning, techniques to achieve high-level semantic understanding (clustering, classification, prediction, etc.) of biomedical signals. Such semantic understanding is not limited to visual signals (images, videos), but can be performed on any other non-visual data sources.
Our research covers both development of novel algorithms for biomedical image/pattern analysis and understanding, and their application to challenging biomedical problems.
Vita
- 1979 - 1983: Studies in Computer Science, Peking University, China
- 1985 - 1989: Doctoral studies in Computer Science, University of Bern, Switzerland
- 1989 - 1997: Lecturer, University of Bern, Switzerland
- 1997 - 1999: Senior Lecturer, University of Bern, Switzerland
- 2000 - 2001: Research scientist, Kantonsspital St. Gallen, Switzerland
- 1/2002 - 9/2002: Associate Professor (C3) of Computer Science, Technische Universität Berlin
- Since 10/2002: Full Professor (C4) of Computer Science, University of Münster
- Since 2016: Dean of the Faculty of Mathematics and Computer Science, , University of Münster
Selected references
Elischberger F, Bamberg B, Jiang X (2022). Deep learning based detection of segregations for ultrasonic testing. IEEE Transactions on Instrumentation and Measurement. Accepted.
Bian A, Jiang X, Berh D, Risse B (2021). Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3): 1184-1194.
Drees D, Scherzinger A, Hägerling R, Kiefer F, Jiang X (2021). Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. BMC Bioinformatics, 22: 346.
Xu J, Liu J, Zhang D, Zhou Z, Jiang X, Zhang C, Chen X (2021). Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates. International Journal of Computer Assisted Radiology and Surgery, 16: 1785-1794.
Tenbrinck D, Jiang X (2015). Image segmentation with arbitrary noise models by solving minimal surface problems. Pattern Recognition, 48(11): 2393-3309.
Links