AI4Life-MDC24 Challenge team¶

• Vera Galinova - Fondazione Human Technopole, Italy

• Mehdi Seifi - Fondazione Human Technopole, Italy

• Edoardo Giacomello - Fondazione Human Technopole, Italy

• Joran Deschamps - Fondazione Human Technopole, Italy

• Alexander Krull - University of Birmingham, UK

• Florian Jug - Fondazione Human Technopole, Italy

• Beatriz Serrano-Solano - Euro-BioImaging ERIC Bio-Hub, Germany

• AI4Life Consortium

Datasets used¶

• Weigert, M., Schmidt, U., Boothe, T. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods 15, 1090–1097 (2018). https://doi.org/10.1038/s41592-018-0216-7

• Guillaume Jacquemet. (2021). Noisy nuclei dataset for testing deep learning-based denoising tools. Zenodo. https://doi.org/10.5281/zenodo.5750174

• Image set BBBC006v1 from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]. https://bbbc.broadinstitute.org/BBBC006

• Zhang, Y., Zhu, Y., Nichols, E., Wang, Q., Zhang, S., Smith, C., & Howard, S. (2019). A poisson-gaussian denoising dataset with real fluorescence microscopy images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11710-11718).https://arxiv.org/abs/1812.10366