Publications 2022

2022

• Beyene, M., Toussaint, P. A., Thiebes, S., et al. (2022), A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research, Journal of the American Medical Informatics Association 29(8), 1433-1444.

• Hajiabadi, H., Mamontova, I., Prizak, R., et al. (2022), Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters, PNAS Nexus 1(3).

• Löffler, K., and Mikut, R. (2022), EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths, IEEE Access 10, 77147-77157.

• Marinov, Z., Roitberg, A., Schneider, D., et al. (2022), ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization, arXiv:2208.09414.

• Marinov, Z., Schneider, D., Roitberg, A., et al. (2022), Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living, arXiv:2208.01910.

• Seidlitz, S., Sellner, J., Odenthal, J., et al. (2022), Robust deep learning-based semantic organ segmentation in hyperspectral images, Medical Image Analysis 80, 102488.

• Studier-Fischer, A., Seidlitz, S., Sellner, J., et al. (2022), Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model, Scientific Reports 12(11028).

• Toussaint, P. A., Renner, M., Lins, S., et al. (2022), Direct-to-Consumer Genetic Testing in Social Media: Analysis of YouTube Users' Comments (Preprint), JMIR Infodemiology 2(2), e38749.

• Toussaint, P. A., Thiebes, S., Schmidt-Kraepelin, M., et al. (2022), Perceived fairness of direct-to-consumer genetic testing business models, Electronic Markets.

• Warsinsky, S., Schmidt-Kraepelin, M., Thiebes, S., et al. (2022), 'Gamified Expert Annotation Systems: Meta-Requirements and Tentative Design', in Drechsler, Andreas and Gerber, Aurona and Hevner, Alan, ed, The Transdisciplinary Reach of Design Science Research, May 2022, pp. 154-166.

• Zaunseder, E., Haupt, S., Mütze, U., et al. (2022), Opportunities and challenges in machine learning-based newborn screening - A systematic literature review, JIMD Reports 63(3), 250-261.