Roadmap on deep learning for microscopy
Review article, 2026
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.
AI
deep learning
microscopy
imaging
Author
Giovanni Volpe
Institution of physics at Gothenburg University
University of Gothenburg
Carolina Wahlby
Uppsala University
Lei Tian
Boston Univ, Dept Biomed Engn
Boston University
Michael Hecht
Ctr Adv Syst Understanding CASUS
University of Wrocław
Helmholtz Zentrum Dresden Rossendorf e V HZDR
Artur Yakimovich
Helmholtz Zentrum Dresden Rossendorf e V HZDR
Roche Diagnost GmbH
University College London (UCL)
University of Wrocław
Ctr Adv Syst Understanding CASUS
Artificial Intelligence Life Sci CIC
Kristina Monakhova
University of California
Laura Waller
University of California
Ivo F. Sbalzarini
Technische Universität Dresden
Ctr Syst Biol Dresden
Christopher A. Metzler
University of Maryland
Mingyang Xie
University of Maryland
Kevin Zhang
University of Maryland
Isaac C. D. Lenton
University of Queensland
Inst Sci & Technol Austria ISTA
Halina Rubinsztein-Dunlop
University of Queensland
ARC CoE Quantum Biotechnol
Daniel Brunner
Univ Franche Comte
Bijie Bai
Univ Calif Los Angeles, Dept Elect & Comp Engn
Aydogan Ozcan
Univ Calif Los Angeles, Dept Elect & Comp Engn
Daniel Midtvedt
University of Gothenburg
Hao Wang
Boston University
Tongyu Li
Boston University
Natasa Sladoje
Uppsala University
Joakim Lindblad
Uppsala University
Jason T. Smith
ECS Fed
Rensselaer Polytech Inst, Dept Biomed Engn
Marien Ochoa
Univ Wisconsin Madison, Dept Med Phys
Uppsala University
Margarida Barroso
Albany Med Coll, Dept Mol & Cellular Physiol
Xavier Intes
Rensselaer Polytech Inst, Dept Biomed Engn
Tong Qiu
MIT, Res Lab Elect Elect Engn & Comp Sci
Li-Yu Yu
MIT, Res Lab Elect Elect Engn & Comp Sci
Sixian You
MIT, Res Lab Elect Elect Engn & Comp Sci
Yongtao Liu
Oak Ridge Natl Lab, Ctr Nanophase Mat Sci
Maxim A. Ziatdinov
Oak Ridge Natl Lab, Ctr Nanophase Mat Sci
Oak Ridge National Laboratory
Kalinin
Univ Tennessee, Dept Mat Sci & Engn
Arlo Sheridan
Salk Institute for Biological Studies
Uri Manor
Salk Institute for Biological Studies
Univ Calif San Diego, Halicioglu Data Sci Inst
Univ Calif San Diego, Dept Cell & Dev Biol
Elias Nehme
Technion IIT
Ofri Goldenberg
Technion IIT
Yoav Shechtman
Technion IIT
Henrik Klein Moberg
Chalmers, Physics, Chemical Physics
Christoph Langhammer
Chalmers, Physics, Chemical Physics
Barbora Spackova
Czech Academy of Sciences
Saga Helgadottir
Uppsala University
Stockholm University
Benjamin Midtvedt
University of Gothenburg
Aykut Argun
University of Gothenburg
Tobias Thalheim
Leipzig University
Frank Cichos
Leipzig University
Stefano Bo
Max Planck Society
King's College London
Lars Hubatsch
Max Planck Society
Jesus Pineda
University of Gothenburg
Carlo Manzo
Univ Cent Catalunya UV UCC
Inst Recerca i Innovacio Ciencies Vida i Salut Cat
Harshith Bachimanchi
University of Gothenburg
Erik Selander
Lund University
Antoni Homs-Corbera
SAS
OWL Lifesci
Martin Franzl
Leipzig University
Kevin De Haan
Univ Calif Los Angeles, Dept Elect & Comp Engn
Yair Rivenson
Univ Calif Los Angeles, Dept Elect & Comp Engn
Zofia Korczak
University of Gothenburg
Caroline Beck Adiels
University of Gothenburg
Mite Mijalkov
Karolinska Institutet
Daniel Vereb
Karolinska Institutet
Yu-Wei Chang
University of Gothenburg
Joana B. Pereira
Karolinska Institutet
Damian Matuszewski
Uppsala University
Gustaf Kylberg
VINNOVA Swedens Innovat Agcy
Ida-Maria Sintorn
Uppsala University
VINNOVA Swedens Innovat Agcy
Juan C. Caicedo
Univ Wisconsin Madison, Morgridge Inst Res
Beth A. Cimini
Broad Inst Harvard & MIT, Canc Program
Muyinatu A. Lediju Bell
Johns Hopkins Univ, Dept Biomed Engn & Comp Sci
Johns Hopkins University
Bruno M. Saraiva
Inst Gulbenkian Ciencias
Guillaume Jacquemet
Åbo Akademi
University of Turku
Ricardo Henriques
Inst Gulbenkian Ciencias
University College London (UCL)
Wei Ouyang
Royal Institute of Technology (KTH)
Trang Le
Stanford Univ, Dept Bioengn
Estibaliz Gomez-de-Mariscal
Nova University of Lisbon
Inst Gulbenkian Ciencias
Gulbenkian Inst Mol Med
Daniel Sage
Swiss Federal Institute of Technology in Lausanne (EPFL)
Arrate Munoz-Barrutia
Inst Invest Sanit Gregorio Maranon
Universidad Carlos III de Madrid
Ebba Josefson Lindqvist
AI Sweden
Johanna Bergman
AI Sweden
JOURNAL OF PHYSICS-PHOTONICS
2515-7647 (ISSN)
Vol. 8 1 012501Subject Categories (SSIF 2025)
Medical Imaging
DOI
10.1088/2515-7647/ae0fd1