Zijin Yang, M. Sc.
- Since 08/2020:
Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg in Cooperation with University of Würzburg
- 10/2017 – 06/2020:
M. Sc. Medical Engineering (Medical Image and Data Processing), FAU Erlangen-Nürnberg
Intelligent MR Diagnosis of the Liver by Linking Model and Data-driven Processes (iDELIVER)
(Third Party Funds Single)Term: August 3, 2020 - March 31, 2023
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
The project examines the use and further development of machine learning methods for MR image reconstruction and for the classification of liver lesions. Based on a comparison model and data-driven image reconstruction methods, these are to be systematically linked in order to enable high acceleration without sacrificing diagnostic value. In addition to the design of suitable networks, research should also be carried out to determine whether metadata (e.g. age of the patient) can be incorporated into the reconstruction. Furthermore, suitable classification algorithms on an image basis are to be developed and the potential of direct classification on the raw data is to be explored. In the long term, intelligent MR diagnostics can significantly increase the efficiency of use of MR hardware, guarantee better patient care and set new impulses in medical technology.
Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data
German Workshop on Medical Image Computing, 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (ed.): Informatik aktuell 2021
, , , :
This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. Information regarding the online teaching will be added to the studon course
|Srividhya Sathya Narayanan||AI-based classification of diffuse liver disease||MA thesis||finished|
|Arka Nandi||Towards integration of prior knowledge using spatial transformers for segmentation||MA thesis||running|