424 citations
- University of CopenhagenDK19 papers
- Copenhagen University HospitalDK14 papers
- Technical University of DenmarkDK7 papers
- Gentofte HospitalDK4 papers
- Stanford UniversityUS3 papers
- Aarhus UniversityDK2 papers
- Erasmus MCNL2 papers
- University College LondonGB2 papers
- Aalborg UniversityDK1 paper
- Aalborg University HospitalDK1 paper
- Aarhus University HospitalDK1 paper
- Acibadem City ClinicBG1 paper
30 papers
Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review
Andreas W. Aspe, Jonas Jalili Pedersen, Andreas Ohrt Johansen +4
This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT)…
Designing and evaluating advanced adaptive randomised clinical trials: a practical guide
Anders Granholm, Aksel Karl Georg Jensen, Theis Lange +3
Background: Advanced adaptive randomised clinical trials are increasingly used. Compared to their conventional counterparts, their flexibility may make them more efficient, increas…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
"It depends": Configuring AI to Improve Clinical Usefulness Across Contexts
Hubert D. Zając, Jorge M. N. Ribeiro, Silvia Ingala +6
Artificial Intelligence (AI) repeatedly match or outperform radiologists in lab experiments. However, real-world implementations of radiological AI-based systems are found to provi…
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses
Brice Ozenne, Martin Norgaard, Cyril Pernet +1
Even though novel imaging techniques have been successful in studying brain structure and function, the measured biological signals are often contaminated by multiple sources of no…
Data harvesting vs data farming: A study of the importance of variation vs sample size in deep learning-based auto-segmentation for breast cancer patients
ES Buhl, E Maae, LW Matthiessen +7
The aim of this study was to investigate the difference in output, when training a model in three different scenarios: a large clinical delineated data set (with 700/78 patients fo…