9.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Heidelberg UniversityDE146 papers
- German Cancer Research CenterDE68 papers
- National Center for Tumor DiseasesDE37 papers
- University Medical Center UtrechtNL18 papers
- Technical University of MunichDE15 papers
- Else Kröner Fresenius Center for Digital HealthDE14 papers
- Harvard UniversityUS14 papers
- University Hospital Carl Gustav CarusDE14 papers
- University Medical Centre MannheimDE14 papers
- RWTH Aachen UniversityDE13 papers
- Utrecht UniversityNL13 papers
- Karlsruhe Institute of TechnologyDE12 papers
11 papers · 1 filter
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
Maximilian Ferle, Jonas Ader, Thomas Wiemers +7
Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We…
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
Héctor Cadavid, Hyunho Mo, Bauke Arends +5
Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework t…
AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel +10
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Gi…
Deep learning empowered sensor fusion boosts infant movement classification
Tomas Kulvicius, Dajie Zhang, Luise Poustka +8
To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia +4
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical da…
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
Georgii Kostiuchik, Lalith Sharan, Benedikt Mayer +3
Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of inter…
Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Soroosh Tayebi Arasteh, Peter Isfort, Marwin Saehn +6
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Video-rate multispectral imaging in laparoscopic surgery: First-in-human application
Leonardo Ayala, Sebastian Wirkert, Anant Vemuri +6
Multispectral and hyperspectral imaging (MSI/HSI) can provide clinically relevant information on morphological and functional tissue properties. Application in the operating room (…
Learning High-Order Interactions via Targeted Pattern Search
Michela C. Massi, Nicola R. Franco, Francesca Ieva +3
Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent fro…
Blending LSTMs into CNNs
Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana +6
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN wi…