Investigating human skin using deep learning enhanced multiphoton microscopy

Mikko J. Huttunen; Radu Hristu; Adrian Dumitru; Mariana Costache; Stefan G. Stanciu

doi:10.1109/ICTON.2019.8840265

Investigating human skin using deep learning enhanced multiphoton microscopy

Mikko J. Huttunen, Radu Hristu, Adrian Dumitru, Mariana Costache, Stefan G. Stanciu

Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	21st International Conference on Transparent Optical Networks, ICTON 2019
Publisher	IEEE
ISBN (Electronic)	9781728127798
DOIs	https://doi.org/10.1109/ICTON.2019.8840265
Publication status	Published - 1 Jul 2019
Publication type	A4 Article in conference proceedings
Event	International Conference on Transparent Optical Networks - Angers, France Duration: 9 Jul 2019 → 13 Jul 2019

Publication series

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

Conference

Conference	International Conference on Transparent Optical Networks
Country/Territory	France
City	Angers
Period	9/07/19 → 13/07/19

Keywords

Deep learning
Label-free
Machine learning
Nonlinear microscopy
Tissue characterization

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICTON.2019.8840265

Cite this

@inproceedings{d153b034a11549e884627d1c5774aa4b,

title = "Investigating human skin using deep learning enhanced multiphoton microscopy",

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",

author = "Huttunen, {Mikko J.} and Radu Hristu and Adrian Dumitru and Mariana Costache and Stanciu, {Stefan G.}",

note = "jufoid=72297; International Conference on Transparent Optical Networks ; Conference date: 09-07-2019 Through 13-07-2019",

year = "2019",

month = jul,

day = "1",

doi = "10.1109/ICTON.2019.8840265",

language = "English",

series = "International Conference on Transparent Optical Networks",

publisher = "IEEE",

booktitle = "21st International Conference on Transparent Optical Networks, ICTON 2019",

}

Huttunen, MJ, Hristu, R, Dumitru, A, Costache, M & Stanciu, SG 2019, Investigating human skin using deep learning enhanced multiphoton microscopy. in 21st International Conference on Transparent Optical Networks, ICTON 2019. International Conference on Transparent Optical Networks, IEEE, International Conference on Transparent Optical Networks, Angers, France, 9/07/19. https://doi.org/10.1109/ICTON.2019.8840265

Investigating human skin using deep learning enhanced multiphoton microscopy. / Huttunen, Mikko J.; Hristu, Radu; Dumitru, Adrian et al.

21st International Conference on Transparent Optical Networks, ICTON 2019. IEEE, 2019. (International Conference on Transparent Optical Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Investigating human skin using deep learning enhanced multiphoton microscopy

AU - Huttunen, Mikko J.

AU - Hristu, Radu

AU - Dumitru, Adrian

AU - Costache, Mariana

AU - Stanciu, Stefan G.

N1 - jufoid=72297

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

KW - Deep learning

KW - Label-free

KW - Machine learning

KW - Nonlinear microscopy

KW - Tissue characterization

U2 - 10.1109/ICTON.2019.8840265

DO - 10.1109/ICTON.2019.8840265

M3 - Conference contribution

AN - SCOPUS:85073067873

T3 - International Conference on Transparent Optical Networks

BT - 21st International Conference on Transparent Optical Networks, ICTON 2019

PB - IEEE

T2 - International Conference on Transparent Optical Networks

Y2 - 9 July 2019 through 13 July 2019

ER -

Investigating human skin using deep learning enhanced multiphoton microscopy

Abstract

Publication series

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

UN SDGs

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Cite this