Zobrazeno 1 - 10
of 348
pro vyhledávání: '"Beqiri P"'
Autor:
Brajshori Naime, Beqiri Petrit, Hajrullahu Vjosë, Ajeti Ali, Ismajli Ardita, Behrami Astrit, Zenelaj Djellza, Zeka Kaltrina, Cahani Klara
Publikováno v:
E3S Web of Conferences, Vol 585, p 06003 (2024)
This study investigates the knowledge and expectations regarding Advanced Nurse Practitioners (ANPs) in Kosovo’s hospital environment, aiming to enhance healthcare standards to meet European benchmarks. Drawing insights from leading European nation
Externí odkaz:
https://doaj.org/article/8fda55b83f3e4442aa07b5d513563b9c
We investigate the utility of diffusion generative models to efficiently synthesise datasets that effectively train deep learning models for image analysis. Specifically, we propose novel $\Gamma$-distribution Latent Denoising Diffusion Models (LDMs)
Externí odkaz:
http://arxiv.org/abs/2409.19371
Autor:
Bransby, Kit M., Kim, Woo-jin Cho, Oliveira, Jorge, Thorley, Alex, Beqiri, Arian, Gomez, Alberto, Chartsias, Agisilaos
Building an echocardiography view classifier that maintains performance in real-life cases requires diverse multi-site data, and frequent updates with newly available data to mitigate model drift. Simply fine-tuning on new datasets results in "catast
Externí odkaz:
http://arxiv.org/abs/2407.21577
Autor:
Highton, Jack, Chong, Quok Zong, Finestone, Samuel, Beqiri, Arian, Schnabel, Julia A., Bhatia, Kanwal K.
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the training data, models may unexpectedly fail when cases differ from th
Externí odkaz:
http://arxiv.org/abs/2406.19557
Autor:
Bransby, Kit Mills, Beqiri, Arian, Kim, Woo-Jin Cho, Oliveira, Jorge, Chartsias, Agisilaos, Gomez, Alberto
Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reason. This undesired learning of naive shortcuts (Clever Hans effect) can
Externí odkaz:
http://arxiv.org/abs/2406.19148
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real da
Externí odkaz:
http://arxiv.org/abs/2305.05424
Autor:
Agron Dogjani, Kastriot Haxhirexha, Juan Carlos Puyana, Chrysanthos Georgiou, Diego Mariani, Mauro Zago, Roman Pfeifer, Michel Teuben, Alba Shehu, Ilir Hasani, Ennio Adami, Arben Beqiri, Basri Lenjani
Publikováno v:
Albanian Journal of Trauma and Emergency Surgery, Vol 8, Iss 2.8 (2024)
The 8th Albanian Congress of Trauma and Emergency Surgery (ACTES 2024) was a dynamic platform for exchanging cutting-edge knowledge, clinical expertise, and innovative trauma and emergency surgery research. This prestigious event will convene leading
Externí odkaz:
https://doaj.org/article/e3465dffcc1e4fbf87dc4123a5ff0085
Autor:
Stojanovski, David, Hermida, Uxio, Muffoletto, Marica, Lamata, Pablo, Beqiri, Arian, Gomez, Alberto
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires
Externí odkaz:
http://arxiv.org/abs/2207.13424
Autor:
Judge, Thierry, Bernard, Olivier, Porumb, Mihaela, Chartsias, Agis, Beqiri, Arian, Jodoin, Pierre-Marc
Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. The independent pixel-wise uncertainty estimate
Externí odkaz:
http://arxiv.org/abs/2206.07664
Autor:
Reynaud, Hadrien, Vlontzos, Athanasios, Dombrowski, Mischa, Lee, Ciarán, Beqiri, Arian, Leeson, Paul, Kainz, Bernhard
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the case of echocardiograms by looking into the variation of the Left Ventric
Externí odkaz:
http://arxiv.org/abs/2206.01651