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master's thesis
Duboko učenje za analizu slika retine
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:614154
Pavlić, Šime
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Pavlić, Š. (2020). Duboko učenje za analizu slika retine (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:614154

Pavlić, Šime. "Duboko učenje za analizu slika retine." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:614154

Pavlić, Šime. "Duboko učenje za analizu slika retine." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:614154

Pavlić, Š. (2020). 'Duboko učenje za analizu slika retine', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 04 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:614154

Pavlić Š. Duboko učenje za analizu slika retine [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 November 04] Available at: https://urn.nsk.hr/urn:nbn:hr:168:614154

Š. Pavlić, "Duboko učenje za analizu slika retine", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:614154

Metadata
TitleDuboko učenje za analizu slika retine
Title (english)Deep Learning for Retinal Image Analysis
AuthorŠime Pavlić
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 country2020-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Posljednjih godina, sve se više istražuje područje računalno potpomognute medicinske dijagnostike. Za veliko poboljšanje u rezultatima problema detekcije i segmentacije, zaslužan je strelovit razvoj metoda dubokog učenja. U ovom radu fokus je na problemu segmentacije tekućina na OCT slikama mrežnice koristeći nove vrste umjetnih neuronskih mreža, kapsularne neuronske mreže. Dan je pregled dosadašnjih ostvarenja na području segmentacije OCT slika mrežnice i kapsularnih neuronskih mreža. Za potrebe rada, u Keras-u je implementirana relativno nova varijacija kapsularnih mreža, Matwo-CapsNet. Performanse implementacije testirane su na podskupu RETOUCH baze podataka OCT slika mrežnice. Dobiveni su vrlo dobri rezultati, mjerljivi s ponajboljim metodama koje su sudjelovale u originalnom RETOUCH izazovu.
Abstract (english)
In the last few years, there has been an increasing interest in the field of computer-assisted medical diagnostics. The rapid development of deep learning methods is responsible for the great improvement in the results of detection and segmentation problems. In this paper, the focus is on the segmentation of fluids on retinal OCT images using new types of artificial neural networks, capsule neural networks. Firstly, we present an overview of previous achievements in the fields of segmentation of OCT retinal images and capsule neural networks. Secondly, we present the implementation of a relatively new variant of capsule networks, Matwo-CapsNet, that has been implemented in Keras for the purposes of this paper. Testing was performed on the subset of RETOUCH database of OCT retinal images. Acquired results are very satisfactory, comparable to the results of best methods participating in the original RETOUCH challenge.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:614154
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
Access conditionsClosed access
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Created on2023-01-13 10:33:59