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master's thesis
Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:217654
Cvitanović, Petar
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Cvitanović, P. (2024). Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:217654

Cvitanović, Petar. "Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:217654

Cvitanović, Petar. "Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:217654

Cvitanović, P. (2024). 'Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:217654

Cvitanović P. Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:217654

P. Cvitanović, "Predtreniranje dubokih neuronskih mreža na analizu medicinskih slika", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:217654

Metadata
TitlePredtreniranje dubokih neuronskih mreža na analizu medicinskih slika
Title (english)Pretraining of deep neural networks used for medical image analysis
AuthorPetar Cvitanović
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-15, Croatia
Abstract
Nakon transplantacije srca, moguća su odbacivanja transplantanta. Proces odbaciva- nja presađenog organa bitno je detektirati i tretirati ako je potrebno. Za tu svrhu, biop- sijom i histološkom analizom prikupljene su slike srca nakon transplantacije na kojima je vidljiva struktura srčanog tkiva. Kroz rad je implementiran model neuronske mreže treniran na skupu slika srca kako bi mogao prepoznati odbacivanje i klasificirati ga u jednu od četiri klase. Model neuronske mreže također je proširen multiple instance lear- ning metodom koja dozvoljava da model treniramo na skupu slika, umjesto pojedinač- nim slikama, za što imamo pripremljen skup podataka. Modeli s i bez multiple instance learning metode su nakon treniranja evaluirani i uspoređeni po svojim F1 mjerama i vrijednostima funkcije gubitka.
Abstract (english)
After a heart transplant, graft rejections are possible. The process of rejection of the transplanted organ is essential to detect and treat if necessary. For this purpose, biopsy and histological analysis were used to collect images of the heart after transplantation, showing the structure of the heart tissue. The paper explains the implementation of a neural network model trained to recognize rejection and classify it into one of four classes depending on the rejection severity. The neural network model is also extended with the multiple instance learning method, which allows us to train the model on a set of images instead of individual images, for which we have a prepared data set. Models with and without the multiple instance learning method were evaluated after training and compared according to their F1 measures and loss function values.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:217654
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 20:18:46