izvorni znanstveni rad
The success of deep learning depends, among other things, on a large amount of labeled data. However, in medical applications, large labeled datasets are the exception, rather than the rule. Manual image labeling is time-consuming and is generally performed only with the purpose of developing algorithms, and not as a part of standard clinical practice. The goal of this study is twofold. Since there is always a trade-off between the ability to collect data and achieve the best possible performance, we wanted to explore how performance depends on the amount of data. For this purpose, a database of manually annotated OCT images was collected. Also, we wanted to see how much transfer learning can help. Retinal OCT images vary depending on the type of device, therefore developed methods should be as robust as possible. Transfer learning was performed so that the model was trained with similar OCT images and then fine-tuned with images from the collected database. It has been shown that transfer learning helps in terms of generalization and better prediction in case the source database is similar to the target database. We can also assume that further improvement can probably be achieved by adding images from another distribution (medical or nonmedical).
deep learning ; retinal OCT images ; image segmentation ;
Razvojem virtualizacijskih tehnologija raste kompleksnost IT sustava, dok korisnici istovremeno zahtijevaju brži pristup resursima, napredne integracije i jednostavnost korištenja. Jedno od ključnih rješenja za ove izazove primjena je…
Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
