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master's thesis
Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja
Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:645201
Ćošković, Hrvoje
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Ćošković, H. (2021). Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:645201

Ćošković, Hrvoje. "Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:645201

Ćošković, Hrvoje. "Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:645201

Ćošković, H. (2021). 'Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja', Master's thesis, University of Zagreb, Faculty of Science, accessed 29 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:645201

Ćošković H. Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2024 December 29] Available at: https://urn.nsk.hr/urn:nbn:hr:217:645201

H. Ćošković, "Segmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:645201

Metadata
TitleSegmentacija stanica na mikroskopskim slikama zasnovana na modelima dubokog učenja
Title (english)Cell segmentation on microscopic images based on deep learning models
AuthorHrvoje Ćošković
MentorTomislav Šmuc (mentor)
Committee memberTomislav Šmuc (predsjednik povjerenstva)
Committee memberZvonimir Bujanović (član povjerenstva)
Committee memberMladen Jurak (član povjerenstva)
Committee memberGoran Igaly (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2021-03-04, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U uvodu ovoga diplomskog rada mogu se pronaći valjani razlozi okretanja strojnom i dubokome učenju, njegove sve veće prednosti nad tradicionalnim metodama, ali i pokoja mana. Zatim smo teoretski skupa prošli cijeli proces dubokog učenja, od modeliranja problema, samog procesa učenja pa i do najsitnijih gradivnih jedinica svake neuronske mreže, njenih slojeva i principa rada i učenja. Nakon toga smo se okrenuli konkretnoj primjeni dubokog učenja u medicini, segmentiranju slika stanica nastalih svjetlosnom elektromikroskopijom stanica iz različitih vrsta tkiva koja su se našla na natjecanju Data Science Bowlu 2018. Vidjeli smo i opisali najuspješnije algoritme i metode koje su natjecatelji koristili, a slobodno možemo reći i najsuvremenija rješenja koja su neki od njih implementirali. Zatim smo došli i do najvažnijeg dijela ovoga rada, procesa modeliranja autorovog modela koji se okušao u segmentaciji slika tumora na centralnom živčanom sustavu kućnog miša. Zadnje poglavlje obuhvaća sve korake i procese u nastanku istoga, te rezultate koji su na nekim uzorcima na jako dobrom tragu, ali kod pojedinih ipak sugeriraju da bi se trebali okrenuti kompleksnijim modelima većeg kapaciteta učenja.
Abstract (english)
In the introduction of this Master work, one can find valid reasons for turning to machine and deep learning, its increasing advantages over traditional methods, but also a few defects. Then, theoretically, we went through the entire process of deep learning, from problem modeling, the learning process itself to the tiniest building units of each neural network, its layers and principles of work and learning. After that, we turned to the concrete application of deep learning in medicine, segmentation of images of cells produced by light electromicroscopy of cells from different types of tissues found in the Data Science Bowl competition in 2018. We have seen and described the most successful algorithms and methods used by competitors, and we can freely say state-of-the-art solutions that some of them have implemented. Then we came to the most important part of this paper, the modelling process of the author’s model that was tested in segmentation of tumor images on the central nervous system of the mouse. The last chapter covers all the steps and processes in the creation of the same, as well as the results that are on some samples on a very good track, but in some cases suggest that they should turn to more complex models of greater learning capacity.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:645201
Study programmeTitle: Computer Science and Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra računarstva i matematike (magistar/magistra računarstva i matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-03-24 11:25:22