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

Fysiikka

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	21st International Conference on Transparent Optical Networks, ICTON 2019
Kustantaja	IEEE
ISBN (elektroninen)	9781728127798
DOI - pysyväislinkit	https://doi.org/10.1109/ICTON.2019.8840265
Tila	Julkaistu - 1 heinäk. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Transparent Optical Networks - Angers, Ranska Kesto: 9 heinäk. 2019 → 13 heinäk. 2019

Julkaisusarja

Nimi	International Conference on Transparent Optical Networks
ISSN (elektroninen)	2161-2064

Conference

Conference	International Conference on Transparent Optical Networks
Maa/Alue	Ranska
Kaupunki	Angers
Ajanjakso	9/07/19 → 13/07/19

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

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

YK:n kestävän kehityksen tavoitteet

Tämä tuotos edistää seuraavia kestävän kehityksen tavoitteita:

Pääsy asiakirjaan

10.1109/ICTON.2019.8840265

Siteeraa tätä

@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",

address = "United States",

}

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

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

Abstrakti

Julkaisusarja

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

YK:n kestävän kehityksen tavoitteet

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä