147 citations · 182 across the 5 of their papers we have counts for
5 papers
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
Dennis Possart, Leonid Mill, Florian Vollnhals +11
Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions…
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Leonid Mill, Oliver Aust, Jochen A. Ackermann +12
Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit th…
Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
Leonid Mill, David Wolff, Nele Gerrits +12
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for prope…
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
Yixing Huang, Fuxin Fan, Christopher Syben +3
Due to the lack of standardized 3D cephalometric analytic methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalom…
Learning with Known Operators reduces Maximum Training Error Bounds
Andreas K. Maier, Christopher Syben, Bernhard Stimpel +7
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain op…