Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
The role of generative adversarial networks in brain MRI: a scoping review
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …
made available. Generative adversarial networks (GANs) showed a lot of potential to …
[HTML][HTML] Enhancing magnetic resonance imaging-driven Alzheimer's disease classification performance using generative adversarial learning
Generative adversarial networks (GAN) can produce images of improved quality but their
ability to augment image-based classification is not fully explored. We evaluated if a …
ability to augment image-based classification is not fully explored. We evaluated if a …
A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques
SN Ahmed, P Prakasam - Progress in Biophysics and Molecular Biology, 2023 - Elsevier
The risk of discovering an intracranial aneurysm during the initial screening and follow-up
screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these …
screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these …
The use of generative adversarial networks in medical image augmentation
A Makhlouf, M Maayah, N Abughanam… - Neural Computing and …, 2023 - Springer
Abstract Generative Adversarial Networks (GANs) have been widely applied in various
domains, including medical image analysis. GANs have been utilized in classification and …
domains, including medical image analysis. GANs have been utilized in classification and …
Challenges for machine learning in clinical translation of big data imaging studies
Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …
many opportunities to neuroscience imaging and epidemiology. However, despite these …
[PDF][PDF] Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images.
B Omarov, A Tursynova, O Postolache… - … Materials & Continua, 2022 - cdn.techscience.cn
The task of segmentation of brain regions affected by ischemic stroke is help to tackle
important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the …
important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the …
A systematic literature review on applications of GAN-synthesized images for brain MRI
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a
popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect …
popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect …
Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review
Contemporary deep learning-based decision systems are well-known for requiring high-
volume datasets in order to produce generalized, reliable, and high-performing models …
volume datasets in order to produce generalized, reliable, and high-performing models …
Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network
Intracranial hemorrhage (ICH) is a serious medical condition that must be diagnosed in a
stipulated time through computed tomography (CT) imaging modality. However, the …
stipulated time through computed tomography (CT) imaging modality. However, the …