Zobrazeno 1 - 10
of 125
pro vyhledávání: '"DUMITRAȘCU OANA"'
Publikováno v:
Analele Universităţii Constantin Brâncuşi din Târgu Jiu : Seria Economie, Vol 1, Iss 2, Pp 123-131 (2024)
The starting premise in proposing and developing this study is that there is an intrinsic conditioning of the development of management science, built around at least three pillars: social phenomena of impact, the perception and internalization of th
Externí odkaz:
https://doaj.org/article/ea52ab2446d14b20b84c7b86c150cf5a
Autor:
TRIF ROBERT-CRISTIAN, DUMITRAȘCU OANA
Publikováno v:
Analele Universităţii Constantin Brâncuşi din Târgu Jiu : Seria Economie, Vol 1, Iss 2, Pp 179-188 (2024)
This paper bibliometrically examines the integration of artificial intelligence (AI) technologies into decisionmaking in management contexts. Using advanced algorithms and machine learning techniques, AI provides transformative capabilities for analy
Externí odkaz:
https://doaj.org/article/4b1dc935cc244fa2920c3d677139784c
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in comple
Externí odkaz:
http://arxiv.org/abs/2410.15036
Retinal fundus photography offers a non-invasive way to diagnose and monitor a variety of retinal diseases, but is prone to inherent quality glitches arising from systemic imperfections or operator/patient-related factors. However, high-quality retin
Externí odkaz:
http://arxiv.org/abs/2409.07862
Autor:
Wang, Hao, Zhu, Wenhui, Qin, Jiayou, Li, Xin, Dumitrascu, Oana, Chen, Xiwen, Qiu, Peijie, Razi, Abolfazl
Detecting retinal image analysis, particularly the geometrical features of branching points, plays an essential role in diagnosing eye diseases. However, existing methods used for this purpose often are coarse-level and lack fine-grained analysis for
Externí odkaz:
http://arxiv.org/abs/2407.12271
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL methods were susceptible to overfit
Externí odkaz:
http://arxiv.org/abs/2308.10112
Autor:
Zhu, Wenhui, Qiu, Peijie, Chen, Xiwen, Li, Xin, Lepore, Natasha, Dumitrascu, Oana M., Wang, Yalin
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the
Externí odkaz:
http://arxiv.org/abs/2306.01289
Autor:
Zhu, Wenhui, Qiu, Peijie, Dumitrascu, Oana M., Sobczak, Jacob M., Farazi, Mohammad, Yang, Zhangsihao, Nandakumar, Keshav, Wang, Yalin
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal
Externí odkaz:
http://arxiv.org/abs/2302.03003
Autor:
Zhu, Wenhui, Qiu, Peijie, Farazi, Mohammad, Nandakumar, Keshav, Dumitrascu, Oana M., Wang, Yalin
Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2302.02991
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motiv
Externí odkaz:
http://arxiv.org/abs/2210.05946