ENBIS Spring Meeting 2024

Session

Invited session

15 May 2024, 10:30

Dortmund

Conveners

Invited session: 1

• Markus Pauly (TU Dortmund University)

56. Trustworthy Federated Learning in Healthcare

Michael Kamp (Institute for AI in Medicine (IKIM), UK Essen)

15/05/2024, 10:30

Spring Meeting

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...

62. Explainable AI and clinical real-world data for personalized cancer treatment

Julius Keyl (UK Essen)

15/05/2024, 10:55

Spring Meeting

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...

63. Interpretable AI in Medicine: Generating Radiological Reports with Panoptic Scene Graphs

Constantin Seibold (University Clinik Essen)

15/05/2024, 11:20

Spring Meeting

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...

54. Machine learning and interpretability methods to investigate Alzheimer's disease

Louise Bloch (Department of Computer Science, University of Applied Sciences and Arts Dortmund)

15/05/2024, 11:45

Spring Meeting

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...