20 citations · 21 across the 9 of their papers we have counts for
10 papers
ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning
Alexander Selivanov, Friederike Jungmann, Jan Kehrer +3
Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardio…
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
Moritz Schlager, Friederike Jungmann, Samuel Schmidgall +12
Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies. However, existin…
Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
Hendrik Möller, Hanna Schön, Alina Dima +10
Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies an…
Body Fat Estimation from Surface Meshes using Graph Neural Networks
Tamara T. Mueller, Siyu Zhou, Sophie Starck +7
Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Fre…
Interpretable 2D Vision Models for 3D Medical Images
Alexander Ziller, Ayhan Can Erdur, Marwa Trigui +9
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly highe…
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann +5
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their at…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
Georgios Kaissis, Moritz Knolle, Friederike Jungmann +3
The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that…
Partial sensitivity analysis in differential privacy
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin +4
Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the pro…
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
Moritz Knolle, Georgios Kaissis, Friederike Jungmann +5
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentat…