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Investigating human skin using deep learning enhanced multiphoton microscopy

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

Details

Original languageEnglish
Title of host publication21st International Conference on Transparent Optical Networks, ICTON 2019
PublisherIEEE
ISBN (Electronic)9781728127798
DOIs
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

Conference

ConferenceInternational Conference on Transparent Optical Networks
CountryFrance
CityAngers
Period9/07/1913/07/19

Abstract

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.

Keywords

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

Publication forum classification

Field of science, Statistics Finland