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Investigating human skin using deep learning enhanced multiphoton microscopy

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication21st International Conference on Transparent Optical Networks, ICTON 2019
ISBN (Electronic)9781728127798
Publication statusPublished - 1 Jul 2019
Publication typeA4 Article in a conference publication
EventInternational Conference on Transparent Optical Networks - Angers, France
Duration: 9 Jul 201913 Jul 2019

Publication series

NameInternational Conference on Transparent Optical Networks
ISSN (Electronic)2161-2064


ConferenceInternational Conference on Transparent Optical Networks


Histopathological image analysis of stained tissue slides is routinely performed by a pathologist to diagnose diseases, such as cancers. Although the approach is effective, it is labor-intensive, time-consuming and risks being biased. Therefore, it would be beneficial to develop faster and more cost-effective approaches. Multiphoton microscopy can alleviate these problems by allowing label-free imaging with high contrast. When label-free multiphoton microscopy is combined with deep learning based image analysis, a wide variety of possibilities arise for the real-time characterization and diagnosis of tissues. Here, we overview our recent work on this topic focusing on automated classification of tissue images taken from human skin near the dermoepidermal junction.


  • Deep learning, Label-free, Machine learning, Nonlinear microscopy, Tissue characterization

Publication forum classification

Field of science, Statistics Finland