Pregled bibliografske jedinice broj: 1251554
Siamese Network for Content-Based Image Retrieval: Detection of Alzheimer's Disease from neuroimaging data
Siamese Network for Content-Based Image Retrieval: Detection of Alzheimer's Disease from neuroimaging data // SoftCOM 2022: 30th International Conference on Software, Telecommunications and Computer Networks: Proceedings / Begušić, Dinko ; Rožić, Nikola ; Radić, Joško ; Šarić, Matko (ur.).
Split: Fakultet elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu, 2022. str. 1-6 doi:10.23919/softcom55329.2022.9911487 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1251554 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Siamese Network for Content-Based Image Retrieval:
Detection of Alzheimer's Disease from neuroimaging
data
Autori
Marin, Ivana ; Marasovic, Tea ; Gotovac, Sven
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
SoftCOM 2022: 30th International Conference on Software, Telecommunications and Computer Networks: Proceedings
/ Begušić, Dinko ; Rožić, Nikola ; Radić, Joško ; Šarić, Matko - Split : Fakultet elektrotehnike, strojarstva i brodogradnje Sveučilišta u Splitu, 2022, 1-6
ISBN
978-953-290-117-7
Skup
30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022
Mjesto i datum
Split, Hrvatska, 22.09.2022. - 24.09.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Alzheimer's Disease ; Deep Learning ; Siamese Neural Network ; Magnetic Resonance Imaging (MRI) ; Content-Based Medical Image Retrieval (CBMIR)
Sažetak
In recent years deep-learning methods have demon- strated impressive results in various domains of computer vision, including medical imaging. This paper examines the possibility of leveraging deep- learning concepts in designing a computer system that could help clinicians make accurate Alzheimer disease (AD) diagnosis by retrieving the most similar archived brain scans of patients with already known diagnoses. We implement a siamese network with ResNet-50 twin subnetworks and train it on the MRI data obtained from ADNI (Alzheimer's Disease Neu-roimaging Initiative) dataset. Four different approaches for slice extraction from MRI volume are considered: using the three slices from the same plane (axial, coronal or sagittal) and combining one slice from each plane. The final performance of the CBIR system on new patient's data based only on MR neuroimaging modality shows limited and comparable performance with all four approaches and leaves space for further enhancements, including complementing neuroimaging MRI data with other data modalities relevant for AD detection.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Prirodoslovno-matematički fakultet, Split
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus