Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Irena Galic"'
Publikováno v:
IEEE Access, Vol 9, Pp 133365-133375 (2021)
A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidneys fall into this category, as
Externí odkaz:
https://doaj.org/article/9eeafdf7a8e247d48399df20cc01f273
Autor:
Marija Habijan, Irena Galic
Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::277b3cc1fe0d382758fca2ab0a8eb02f
http://arxiv.org/abs/2205.09842
http://arxiv.org/abs/2205.09842
Publikováno v:
2022 International Symposium ELMAR, proceedings
Medical image segmentation often requires segmenting multiple elliptical objects on a single image. This includes, among other tasks, segmenting vessels such as the aorta in axial CTA slices. In this paper, we present a general approach to improving
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d4d0d8f98a49bcfc6311638c2b06e2d
https://hdl.handle.net/1854/LU-8760359
https://hdl.handle.net/1854/LU-8760359
Publikováno v:
Biomimetics, Vol 9, Iss 8, p 493 (2024)
Celiac disease, a chronic autoimmune condition, manifests in those genetically prone to it through damage to the small intestine upon gluten consumption. This condition is estimated to affect approximately one in every hundred individuals worldwide,
Externí odkaz:
https://doaj.org/article/795e959512cb4c51810c0a46ac3fc0cb
Autor:
Irena Galić Bešker
Publikováno v:
Latina et Graeca, Vol 2, Iss 38, Pp 39-60 (2020)
Protestantski teolog, filolog, filozof i povjesničar Matija Vlačić Ilirik (1520–1575) većinu je života proveo mijenjajući mjesta boravka izvan rodnog Labina. Zarana prihvativši protestantski nauk, odlazi u zemlje njemačkoga govornog područ
Externí odkaz:
https://doaj.org/article/ff43e0a641404e3c97dd8236de5d881b
Publikováno v:
Sensors, Vol 23, Iss 2, p 633 (2023)
Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We pres
Externí odkaz:
https://doaj.org/article/e768232db81a4bb99ab5ca84edb2dd61
Publikováno v:
Tehnički Vjesnik, Vol 26, Iss 2, Pp 560-565 (2019)
In Human Computer Interaction (HCI) research area, there is an increasing tendency to make devices as simple and as natural as possible for use. These devices are aiming to make input and output techniques, interaction, etc., easier. In the input dom
Externí odkaz:
https://doaj.org/article/dc2d9266ff244532b6637cf2bde89178
Publikováno v:
Applied Sciences, Vol 12, Iss 10, p 5217 (2022)
Epicardial and pericardial adipose tissues (EAT and PAT), which are located around the heart, have been linked to coronary atherosclerosis, cardiomyopathy, coronary artery disease, and other cardiovascular diseases. Additionally, the volume and thick
Externí odkaz:
https://doaj.org/article/6371fa70186447659d62456a69401088
Publikováno v:
Applied Sciences, Vol 12, Iss 6, p 3024 (2022)
Accurate segmentation of cardiovascular structures plays an important role in many clinical applications. Recently, fully convolutional networks (FCNs), led by the UNet architecture, have significantly improved the accuracy and speed of semantic segm
Externí odkaz:
https://doaj.org/article/018462108665492dbbf1130ac66015c6
Publikováno v:
Applied Sciences, Vol 11, Iss 9, p 3912 (2021)
An accurate whole heart segmentation (WHS) on medical images, including computed tomography (CT) and magnetic resonance (MR) images, plays a crucial role in many clinical applications, such as cardiovascular disease diagnosis, pre-surgical planning,
Externí odkaz:
https://doaj.org/article/ffa6dc8320a8424e99d6f30ff091b71b