Roadmap on deep learning for microscopy
: Volpe, Giovanni; Wahlby, Carolina; Tian, Lei; Hecht, Michael; Yakimovich, Artur; Monakhova, Kristina; Waller, Laura; Sbalzarini, Ivo F.; Metzler, Christopher A.; Xie, Mingyang; Zhang, Kevin; Lenton, Isaac C. D.; Rubinsztein-Dunlop, Halina; Brunner, Daniel; Bai, Bijie; Ozcan, Aydogan; Midtvedt, Daniel; Wang, Hao; Li, Tongyu; Sladoje, Natasa; Lindblad, Joakim; Smith, Jason T.; Ochoa, Marien; Barroso, Margarida; Intes, Xavier; Qiu, Tong; Yu, Li-Yu; You, Sixian; Liu, Yongtao; Ziatdinov, Maxim A.; Kalinin, Sergei, V; Sheridan, Arlo; Manor, Uri; Nehme, Elias; Goldenberg, Ofri; Shechtman, Yoav; Moberg, Henrik K.; Langhammer, Christoph; Spackova, Barbora; Helgadottir, Saga; Midtvedt, Benjamin; Argun, Aykut; Thalheim, Tobias; Cichos, Frank; Bo, Stefano; Hubatsch, Lars; Pineda, Jesus; Manzo, Carlo; Bachimanchi, Harshith; Selander, Erik; Homs-Corbera, Antoni; Franzl, Martin; De Haan, Kevin; Rivenson, Yair; Korczak, Zofia; Adiels, Caroline Beck; Mijalkov, Mite; Vereb, Daniel; Chang, Yu-Wei; Pereira, Joana B.; Matuszewski, Damian; Kylberg, Gustaf; Sintorn, Ida-Maria; Caicedo, Juan C.; Cimini, Beth A.; Lediju Bell, Muyinatu A.; Saraiva, Bruno M.; Jacquemet, Guillaume; Henriques, Ricardo; Ouyang, Wei; Le, Trang; Gomez-de-Mariscal, Estibaliz; Sage, Daniel; Munoz-Barrutia, Arrate; Lindqvist, Ebba Josefson; Bergman, Johanna
: 2026
JPhys photonics
: 012501
: 8
: 1
: 2515-7647
DOI: https://doi.org/10.1088/2515-7647/ae0fd1
: https://iopscience.iop.org/article/10.1088/2515-7647/ae0fd1
: https://research.utu.fi/converis/portal/detail/Publication/515862827
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning (ML) are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap encompasses key aspects of how ML is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of ML for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.
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R.H. and B.S. are supported by Gulbenkian Foundation and received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 101001332), and the European Union through the Horizon Europe program (AI4LIFE project, grant agreement 101057970-AI4LIFE), the European Molecular Biology Organization (EMBO) Installation Grant (EMBO-2020-IG4734), and the Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044). G. J. is supported by the Academy of Finland (G.J. 338537), the Cancer Society of Finland (G.J.), Åbo Akademi University Research Foundation (G.J., CoE CellMech), the Solution for Health strategic funding to Åbo Akademi University (G.J.) and the InFLAMES Flagship Programme of the Academy of Finland (G.J., 337531).
