Publication: Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy
Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy
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Gomariz, A., Portenier, T., Helbling, P. M., Isringhausen, S., Suessbier, U., Nombela-Arrieta, C., & Goksel, O. (2021). Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy. Nature Machine Intelligence, 3(9), 799–811. https://doi.org/10.1038/s42256-021-00379-y
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Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized as color channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, wh
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- Gomariz, Alvaro
- Portenier, Tiziano
- Helbling, Patrick M
- Isringhausen, Stephan
- Suessbier, Ute
- Nombela-Arrieta, César
- Goksel, Orcun
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Gomariz, A., Portenier, T., Helbling, P. M., Isringhausen, S., Suessbier, U., Nombela-Arrieta, C., & Goksel, O. (2021). Modality Attention and Sampling Enables Deep Learning with Heterogeneous Marker Combinations in Fluorescence Microscopy. Nature Machine Intelligence, 3(9), 799–811. https://doi.org/10.1038/s42256-021-00379-y
