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pro vyhledávání: '"Saad, Muhammad Muneeb"'
Generative Adversarial Networks (GANs) have high computational costs to train their complex architectures. Throughout the training process, GANs' output is analyzed qualitatively based on the loss and synthetic images' diversity and quality. Based on
Externí odkaz:
http://arxiv.org/abs/2405.20987
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to ge
Externí odkaz:
http://arxiv.org/abs/2309.12245
In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasingly being relied upon for data
Externí odkaz:
http://arxiv.org/abs/2308.02505
Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to a
Externí odkaz:
http://arxiv.org/abs/2210.06334
Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as t
Externí odkaz:
http://arxiv.org/abs/2208.05593
Publikováno v:
EMBC 2022 Compwell Workshop
In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental
Externí odkaz:
http://arxiv.org/abs/2208.04705
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to ge
Externí odkaz:
http://arxiv.org/abs/2201.10324
In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Ad
Externí odkaz:
http://arxiv.org/abs/2201.07646
Akademický článek
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Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2022 Jul; Vol. 2022, pp. 2049-2052.