Multimodal Machine Learning for Decision Support Systems

Project members: ,

Funding source: Siemens Healthineers

Abstract

The project aims to identify areas where advanced data analysis and processing methods can be applied to aspects of computer tomography (CT) technology. Furthermore included is the implementation and validation of said methods.In this project, we analyze machine and customer data sent by thousands of high-end medical devices every day. Since potentially relevant Information is often presented in different modalities, the optimal application of fusion techniques is a key factor when extracting insights. 

For more details, see Dietz et al. (2024).

Mixed-Type time series synthesis for different intermodal interaction strengths between an event sequence and a diffusion process.

Mixed-Type time series synthesis for different intermodal interaction strengths between an event sequence and a set of superimposed sine waves.

References

Dietz, S., Altstidl, T., Zanca, D., Eskofier, B., & Nguyen, A. (2024).
How Intermodal Interaction Affects the Performance of Deep Multimodal Fusion for Mixed-Type Time Series.
In Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
IEEE. https://doi.org/10.1109/IJCNN60899.2024.10650421