Zobrazeno 1 - 10
of 413
pro vyhledávání: '"D. Apostolopoulos"'
Autor:
A. Quinlivan, D. Neuen, D. Hansen, W. Stevens, L. Ross, N. Ferdowsi, S. M. Proudman, J. G. Walker, J. Sahhar, G-S. Ngian, D. Apostolopoulos, L. V. Host, G. Major, C. Basnayake, K. Morrisroe, M. Nikpour
Publikováno v:
Arthritis Research & Therapy, Vol 26, Iss 1, Pp 1-8 (2024)
Abstract Background To determine the relationship between gastroesophageal reflux disease (GORD) and its treatment and interstitial lung disease in patients with systemic sclerosis (SSc). Methods SSc patients from the Australian Scleroderma Cohort St
Externí odkaz:
https://doaj.org/article/9b3157335fc64c0da7e5cc0c2bfdda25
Autor:
S. Papaioannou, D. Fitsios, G. Dabos, K. Vyrsokinos, G. Giannoulis, A. Prinzen, C. Porschatis, M. Waldow, D. Apostolopoulos, H. Avramopoulos, N. Pleros
Publikováno v:
IEEE Photonics Journal, Vol 7, Iss 1, Pp 1-10 (2015)
We demonstrate two 8 × 1 silicon ring resonator (RR)-based multiplexers (MUXs) integrated on the same chip for dual-stream 16-channel multiplexing/ demultiplexing applications. Cascaded second-order RRs equipped with microheaters were integrated on
Externí odkaz:
https://doaj.org/article/e95bf1a997b444ee982ebd7dd996ef3d
Autor:
Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Dimitris J. Apostolopoulos, Nikolaos Papandrianos, Elpiniki I. Papageorgiou
Publikováno v:
Big Data and Cognitive Computing, Vol 8, Iss 8, p 85 (2024)
This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detectio
Externí odkaz:
https://doaj.org/article/f092ac60836446f8b8a8d598491ec4d7
Autor:
Nikolaos I. Papandrianos, Anna Feleki, Serafeim Moustakidis, Elpiniki I. Papageorgiou, Ioannis D. Apostolopoulos, Dimitris J. Apostolopoulos
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7592 (2022)
Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model
Externí odkaz:
https://doaj.org/article/b7a7285cd7d541f3b3c16c2e430c4614
Autor:
C. Vagionas, R. Maximidis, I. Stratakos, A. Margaris, A. Mesodiakaki, M. Gatzianas, K. Kanta, P. Toumasis, G. Giannoulis, D. Apostolopoulos, E. A. Papatheofanous, G. Lentaris, D. Reisis, D. Soudris, K. Tsagkaris, N. Argyris, D. Syrivelis, P. Bakopoulos, R. M. Oldenbeuving, C. G. H. Roeloffzen, P. W. L. van Dijk, I. Dimogiannis, A. Kontogiannis, H. Avramopoulos, A. Miliou, N. Pleros, G. Kalfas
Publikováno v:
Journal of Lightwave Technology. 41:1104-1113
Autor:
Nikolaos I. Papandrianos, Ioannis D. Apostolopoulos, Anna Feleki, Serafeim Moustakidis, Konstantinos Kokkinos, Elpiniki I. Papageorgiou
Publikováno v:
Nuclear Medicine Communications. 44:1-11
Autor:
Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Elpiniki I. Papageorgiou, Dimitris J. Apostolopoulos
Publikováno v:
Machine Learning and Knowledge Extraction. 4:814-826
Background: Recent advances in Artificial Intelligence (AI) algorithms, and specifically Deep Learning (DL) methods, demonstrate substantial performance in detecting and classifying medical images. Recent clinical studies have reported novel optical
Autor:
Nikolaos I. Papandrianos, Ioannis D. Apostolopoulos, Anna Feleki, Dimitris J. Apostolopoulos, Elpiniki I. Papageorgiou
Publikováno v:
Annals of Nuclear Medicine. 36:823-833
Autor:
Agorastos-Dimitrios Samaras, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Nikolaos Papandrianos, Elpiniki Papageorgiou
Publikováno v:
Scientific Reports. 13
The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert’s opinion as input, making it a "man-i
Publikováno v:
Information; Volume 14; Issue 3; Pages: 174
This study proposes the integration of attention modules, feature-fusion blocks, and baseline convolutional neural networks for developing a robust multi-path network that leverages its multiple feature-extraction blocks for non-hierarchical mining o