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Roadmap on deep learning for microscopy

Volpe, G. ; Selander, E. LU and Bergman, J. (2026) In JPhys Photonics 8(1).
Abstract
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... (More)
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. © 2026 The Author(s). Published by IOP Publishing Ltd. (Less)
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author
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type
Contribution to journal
publication status
published
subject
keywords
AI, deep learning, imaging, microscopy, Classification (of information), Data quality, Deep neural networks, Image enhancement, Image quality, Image segmentation, Learning systems, Nanotechnology, Deep learning, Digital imaging, Imaging microscopy, Machine-learning, Microscopy images, Nano scale, Neural network learning, Quantitative tool, Roadmap, Visual observations, Microscopic examination
in
JPhys Photonics
volume
8
issue
1
article number
012501
publisher
IOP Publishing
external identifiers
  • scopus:105030837756
ISSN
2515-7647
DOI
10.1088/2515-7647/ae0fd1
language
English
LU publication?
yes
id
e2f90f0a-1f4d-4033-8c17-f66ada16a032
date added to LUP
2026-03-26 11:46:06
date last changed
2026-03-26 11:47:15
@article{e2f90f0a-1f4d-4033-8c17-f66ada16a032,
  abstract     = {{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. © 2026 The Author(s). Published by IOP Publishing Ltd.}},
  author       = {{Volpe, G. and Selander, E. and Bergman, J.}},
  issn         = {{2515-7647}},
  keywords     = {{AI; deep learning; imaging; microscopy; Classification (of information); Data quality; Deep neural networks; Image enhancement; Image quality; Image segmentation; Learning systems; Nanotechnology; Deep learning; Digital imaging; Imaging microscopy; Machine-learning; Microscopy images; Nano scale; Neural network learning; Quantitative tool; Roadmap; Visual observations; Microscopic examination}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{IOP Publishing}},
  series       = {{JPhys Photonics}},
  title        = {{Roadmap on deep learning for microscopy}},
  url          = {{http://dx.doi.org/10.1088/2515-7647/ae0fd1}},
  doi          = {{10.1088/2515-7647/ae0fd1}},
  volume       = {{8}},
  year         = {{2026}},
}