Conveners
Invited session: 1
- Markus Pauly (TU Dortmund University)
Machine learning has transformed many industries, being employed not only on large centralized datasets, but increasingly on data generated by a multitude of networked, complex devices such as mobile phones, autonomous vehicles or industrial machines. However, data-privacy and security concerns often prevent the centralization of this data, most prominently in healthcare. Federated learning...
Although a large amount of data is collected on each patient during cancer care, clinical decisions are mostly based on limited parameters and expert knowledge. This is mainly due to insufficient data infrastructure and a lack of tools to comprehensively integrate diverse clinical data. At University Hospital Essen, medical data is stored in FHIR format, enabling cutting-edge analyses of...
We explore the integration of panoptic scene graphs in the field of chest radiographs, to enhance explainable medical report generation. Panoptic scene graphs require a model to generate a more comprehensive scene graph representation based on panoptic segmentations rather than rigid bounding boxes and thus present a more holistic image representation. These graphs facilitate accurate report...
The use of machine learning methods in clinical settings is increasing. One reason for this is the availability of more complex models that promise more accurate predictive performance, especially for the study of heterogeneous diseases with multimodal data, such as Alzheimer’s disease. However, as machine learning models become more complex, their interpretability decreases. The reduced...