Duboko učenje za analizu slika retine

master's thesis

• Title (english): Deep Learning for Retinal Image Analysis
• Author: Šime Pavlić
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-14, 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: Duboko učenje; Kapsularne neuronske mreže; OCT snimanje; Mrežnica; Segmentacija slike
• Keywords (english): Deep learning; Capsule neural networks; OCT imaging; Retina; Image segmentation
• Language: croatian
• URN:NBN: urn:nbn:hr:168:614154
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 10:33:59