Raman hyperspectral molecular imaging combined with convolutional neural networks in early diagnosis of pancreatic cancer, considering selected, pathologically different, subtypes of the tumor - Digital Medical Library

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Title: Raman hyperspectral molecular imaging combined with convolutional neural networks in early diagnosis of pancreatic cancer, considering selected, pathologically different, subtypes of the tumor

Author:

Szymoński, Krzysztof

Date:

Type:

Praca doktorska

Abstract:

Pancreatic cancer (PC) is one of the most poorly prognostic human cancers, despite continuously deepening knowledge about its biology. The Jack of sensitive and specific early diagnostic methods, combined with the molecular and morphological heterogeneity of the tumor, are the main reasons for the PC patients dreadful prognosis. In the research comprising this doctoral thesis, we developed an innovative methodology for effective imaging of PC tissues, combining molecular microscopy methodolo~ies (Raman hyperspectral mapping) with artificial neutal network techniques. This approach allowed us to characterize the most common subtypes of PC, jdentifying differentiating properties related to protein content and the intensity of epigenetic changes. The developed metho4ology, due to its versatility, could be used to devefop very seńi;itive and specific early diagnostic methods for PC based o:p. analyzir,tg patient blood samples.

Place of publishing:

Level of degree:

2 - studia doktoranckie

Degree discipline:

choroby układu trawiennego ; onkologia ; patomorfologia

Degree grantor:

Rada Dyscypliny Nauki medyczne

Promoter:

Adamek, Dariusz

Date issued:

Identifier:

oai:dl.cm-uj.krakow.pl:5177

Call number:

Language:

Access rights:

tylko w bibliotece

Subject and keywords:

pancreatic cancer molecular imaging convolutional neural networks cancer diagnostics

Object collections:

Digital Medical Library > PhD thesis database

Last modified:

Mar 12, 2025

In our library since:

Dec 30, 2024

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4

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0

All available object's versions:

http://dl.cm-uj.krakow.pl:8080/publication/5178

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Edition name	Date
ZB-141597	Mar 12, 2025

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