Hyppää sisältöön
    • Suomeksi
    • In English
Trepo
  • Suomeksi
  • In English
  • Kirjaudu
Näytä viite 
  •   Etusivu
  • Trepo
  • Opinnäytteet - ylempi korkeakoulututkinto
  • Näytä viite
  •   Etusivu
  • Trepo
  • Opinnäytteet - ylempi korkeakoulututkinto
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Classifying non-small cell lung carcinoma in histological images using a convolutional neural network

Timonen, Veera (2019)

 
Avaa tiedosto
TimonenVeera.pdf (3.743Mt)
Lataukset: 



Timonen, Veera
2019

Bioteknologian tutkinto-ohjelma
Lääketieteen ja terveysteknologian tiedekunta - Faculty of Medicine and Health Technology
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Hyväksymispäivämäärä
2019-05-24
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-201907152578
Tiivistelmä
The purpose of this Master’s thesis was to teach a convolutional neural network to recognize non-small cell lung cancer from whole slide images (WSI) and to separate regions of interest from other tissue. IHC fluoro-chromogenically stained whole slide images under brightfield illumination were used as target images, and the same WSIs with cytokeratin masks applied under fluorescent illumination were used as input images.

An immunohistochemical fluoro-chromogenic dye is done when PD-L1-expressing tumor regions and PD1-expressing alveolar macrophages need to be distinguished. Cytokeratin-positive carcinoma regions show clearly in brightfield images. It is important to separate these regions especially when considering immunotherapy as treatment, because there exist antibody based medications against both PD1- and PD-L1 expressing tumor- and lymphocyte cells, and the areas surrounding cancer may cause false positives leading to immunotherapy being poorly targeted.

The method is based on U-net architecture in a convolutional neural network. A CNN is capable of achieving excellent results in tasks including image recognition, and U-net has been specifically designed for medical image analysis tasks.

The results show that the neural network used is capable of distinguishing cancer regions from other tissue with good accuracy (AUC = 0.96).
Kokoelmat
  • Opinnäytteet - ylempi korkeakoulututkinto [40596]
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste
 

 

Selaa kokoelmaa

TekijätNimekkeetTiedekunta (2019 -)Tiedekunta (- 2018)Tutkinto-ohjelmat ja opintosuunnatAvainsanatJulkaisuajatKokoelmat

Omat tiedot

Kirjaudu sisäänRekisteröidy
Kalevantie 5
PL 617
33014 Tampereen yliopisto
oa[@]tuni.fi | Tietosuoja | Saavutettavuusseloste