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pro vyhledávání: '"Dumitrașcu A"'
Autor:
Diaconu Rodica, Dumitrașcu Andreea-Ramona, Liehn Elisa, Pîrvu Andrei, Ioana Mihai, Alexandru Dragoș, Țieranu Eugen, Bălșeanu Tudor-Adrian, Donoiu Ionuț
Publikováno v:
Romanian Journal of Cardiology, Vol 33, Iss 1, Pp 19-24 (2023)
Background: Apolipoprotein E (ApoE) is a ubiquitous protein involved in maintaining cholesterol homeostasis and lipoprotein clearance from circulation. It is coded by three alleles (ε2, ε3, ε4) with six genotypes (ε3/ε3, ε3/ε4, ε2/ε3, ε4/ε
Externí odkaz:
https://doaj.org/article/62a4242869b843bf87f21c233ab1c2c9
Autor:
Wang, Hao, Zhu, Wenhui, Dong, Xuanzhao, Chen, Yanxi, Li, Xin, Qiu, Peijie, Chen, Xiwen, Vasa, Vamsi Krishna, Xiong, Yujian, Dumitrascu, Oana M., Razi, Abolfazl, Wang, Yalin
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training mul
Externí odkaz:
http://arxiv.org/abs/2412.02825
Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the
Externí odkaz:
http://arxiv.org/abs/2411.01403
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