A2 Refereed review article in a scientific journal
Roadmap on deep learning for microscopy
Authors: 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
Publication year: 2026
Journal: JPhys photonics
Article number: 012501
Volume: 8
Issue: 1
eISSN: 2515-7647
DOI: https://doi.org/10.1088/2515-7647/ae0fd1
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://iopscience.iop.org/article/10.1088/2515-7647/ae0fd1
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515862827
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
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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Funding information in the publication:
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).
