Zobrazeno 1 - 10
of 37 516
pro vyhledávání: '"Fundus İmages"'
Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applicatio
Externí odkaz:
http://arxiv.org/abs/2412.09402
Autor:
Prenner, Andrea
Early detection of cardiovascular disease risk factors is essential to alter the course of the disease. Previous studies showed that deep learning can successfully be used to detect such risk factors from retinal images. This study uses convolutional
Externí odkaz:
http://arxiv.org/abs/2410.11535
Autor:
Zhao, Zhihao, Yang, Junjie, Faghihroohi, Shahrooz, Zhao, Yinzheng, Zapp, Daniel, Huang, Kai, Navab, Nassir, Nasseri, M. Ali
The utilization of longitudinal datasets for glaucoma progression prediction offers a compelling approach to support early therapeutic interventions. Predominant methodologies in this domain have primarily focused on the direct prediction of glaucoma
Externí odkaz:
http://arxiv.org/abs/2410.21130
The diagnosis of diabetic retinopathy, which relies on fundus images, faces challenges in achieving transparency and interpretability when using a global classification approach. However, segmentation-based databases are significantly more expensive
Externí odkaz:
http://arxiv.org/abs/2410.13822
Autor:
Mannepalli, Praveen Kumar1 praveen.e17513@cumail.in, Singh Baghela, Vishwa Deepak2, Agrawal, Alka3, Johri, Prashant2, Dubey, Shubham Satyam2, Parmar, Kapil2
Publikováno v:
Traitement du Signal. Oct2024, Vol. 41 Issue 5, p2459-2470. 12p.
Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial in
Externí odkaz:
http://arxiv.org/abs/2408.08790
Autor:
Quiros, Jose Vargas, Liefers, Bart, van Garderen, Karin, Vermeulen, Jeroen, Center, Eyened Reading, Consortium, Sinergia, Klaver, Caroline
We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public datasets . Additional CFIs, mainly from the population-based Rotterdam Stu
Externí odkaz:
http://arxiv.org/abs/2409.16016
Fundus image classification is crucial in the computer aided diagnosis tasks, but label noise significantly impairs the performance of deep neural networks. To address this challenge, we propose a robust framework, Self-Supervised Pre-training with R
Externí odkaz:
http://arxiv.org/abs/2409.18147
Autor:
Alif, Mujadded Al Rabbani
Retinal fundus imaging plays an essential role in diagnosing various stages of diabetic retinopathy, where exudates are critical markers of early disease onset. Prompt detection of these exudates is pivotal for enabling optometrists to arrest or sign
Externí odkaz:
http://arxiv.org/abs/2408.06784
Optic disc and cup segmentation helps in the diagnosis of glaucoma, myocardial infarction, and diabetic retinopathy. Most deep learning methods developed to perform segmentation tasks are built on top of a U-Net-based model architecture. Nevertheless
Externí odkaz:
http://arxiv.org/abs/2408.05052