Publications

  1. Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge: Valentin Munteanu et al., J Appl Cryst, doi: 10.1107/S1600576724002115
  2. PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms: Erik Genthe et al. et al., Journal of Structural Biology, doi: 10.1016/j.jsb.2023.107990
  3. Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data: Vladimir Starostin et al., npj Comput Mater, doi: 10.1038/s41524-022-00778-8
  4. The osteogenetic activities of mesenchymal stem cells in response to Mg2+ ions and inflammatory cytokines: a numerical approach using fuzzy logic controllers: Jalil Nourisa et al., PLoS Comput Biol, doi: 10.1371/journal.pcbi.1010482
  5. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., J Appl Cryst, doi: 10.1107/S1600576722002230
  6. An active learning approach for the interactive and guided segmentation of tomography data: Bashir Kazimi et al., SPIE Optical Engineering + Applications, doi: 10.1117/12.2637973
  7. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., arXiv:2202.11609 [physics.data-an], doi: 10.48550/arXiv.2202.11609
  8. End-to-End Deep Learning Pipeline for Real-Time Processing of Surface Scattering Data at Synchrotron Facilities: Vladimir Starostin et al., Synchrotron Radiation News, doi: 10.1080/08940886.2022.2112499
  9. PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets: Yuhe Zhang et al., Opt. Express, doi: 10.1364/OE.423222
  10. Machine learning denoising of high-resolution X-ray nanotomography data: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577521011139
  11. X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease: Sebastian Günther et al., Science, doi: 10.1126/science.abf7945
  12. High-resolution ex vivo analysis of the degradation and osseointegration of Mg-xGd implant screws in 3D: Diana Krüger et al., Bioactive Materials, doi: 10.1016/j.bioactmat.2021.10.041
  13. Scaling the U-net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms: Ivo M. Baltruschat et al., Sci Rep, doi: 10.1038/s41598-021-03542-y
  14. Suppression of thermal nanoplasma emission in clusters strongly ionized by hard x-rays: Yoshiaki Kumagai et al., J. Phys. B: At. Mol. Opt. Phys., doi: 10.1088/1361-6455/abd878
  15. Tomographic reconstruction with a generative adversarial network: Xiaogang Yang et al., J Synchrotron Radiat, doi: 10.1107/S1600577520000831
  16. Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface: Anil Kunwar et al., Journal of Materials Science & Technology, doi: 10.1016/j.jmst.2020.04.046
  17. Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577520007407
  18. Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function: Tobias Strohmann et al., Sci Rep, doi: 10.1038/s41598-019-56008-7
  19. A load frame for in situ tomography at PETRA III: Julian Moosmann et al., Proceedings Volume 11113, Developments in X-Ray Tomography XII, doi: 10.1117/12.2530445
  20. Silver clusters shape determination from in-situ XANES data: Janis Timoshenko et al., Radiation Physics and Chemistry, doi: 10.1016/j.radphyschem.2018.11.003
  21. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning: A. Sanchez-Gonzalez et al., Nat Commun, doi: 10.1038/ncomms15461