1 citations · 1 across the 4 of their papers we have counts for
5 papers
Bayesian Optimization for Design Parameters of 3D Image Data Analysis
David Exler, Joaquin Eduardo Urrutia Gómez, Martin Krüger +5
Deep learning-based segmentation and classification are crucial to large-scale biomedical imaging, particularly for 3D data, where manual analysis is impractical. Although many met…
Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
Sarina Penquitt, Tobias Riedlinger, Timo Heller +2
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and indust…
Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters
David Exler, Mark Schutera, Markus Reischl +1
With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispos…
Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI
Yanke Wang, Yolanne Y. R. Lee, Aurelio Dolfini +3
Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been…
Improving 3D deep learning segmentation with biophysically motivated cell synthesis
Roman Bruch, Mario Vitacolonna, Elina Nürnberg +3
Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level…