Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration
Sign Up to like & getrecommendations! Published in 2019 at "JAMA Ophthalmology"
DOI: 10.1001/jamaophthalmol.2018.6156
Abstract: Importance Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks… read more here.
Keywords: data sets; real synthetic; retinal specialists; synthetic images ... See more keywords
A hybrid image dataset toward bridging the gap between real and simulation environments for robotics
Sign Up to like & getrecommendations! Published in 2018 at "Machine Vision and Applications"
DOI: 10.1007/s00138-018-0966-3
Abstract: The primary motivation of computer vision in the robotics field is to obtain a perception level that is as close as possible to human visual system. To achieve this, the inclusion of large datasets is… read more here.
Keywords: real world; dataset; robotics; synthetic images ... See more keywords
Exploring Image Generation for UAV Change Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/CAA Journal of Automatica Sinica"
DOI: 10.1109/jas.2022.105629
Abstract: Change detection (CD) is becoming indispensable for unmanned aerial vehicles (|UAVs), especially in the domain of water landing, rescue and search. However, even the most advanced models require large amounts of data for model training… read more here.
Keywords: change detection; exploring image; synthetic images; change ... See more keywords
Learning From Synthetic Images via Active Pseudo-Labeling
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2020.2989100
Abstract: Synthetic visual data refers to the data automatically rendered by the mature computer graphic algorithms. With the rapid development of these techniques, we can now collect photo-realistic synthetic images with accurate pixel-level annotations without much… read more here.
Keywords: learning synthetic; pseudo; pseudo labeling; synthetic images ... See more keywords
Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.
Sign Up to like & getrecommendations! Published in 2023 at "Radiology"
DOI: 10.1148/radiol.222211
Abstract: Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images.… read more here.
Keywords: contrast; synthetic images; enhanced images; breast mri ... See more keywords
Can Synthetic Images Improve CNN Performance in Wound Image Classification?
Sign Up to like & getrecommendations! Published in 2023 at "Studies in health technology and informatics"
DOI: 10.3233/shti230311
Abstract: For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of… read more here.
Keywords: performance; classification; synthetic images; cnn ... See more keywords
Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2022.954662
Abstract: The assessment of disease activity using serial brain MRI scans is one of the most valuable strategies for monitoring treatment response in patients with multiple sclerosis (MS) receiving disease-modifying treatments. Recently, several deep learning approaches… read more here.
Keywords: new lesion; synthetic images; lesion detection; detection ... See more keywords
Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Personalized Medicine"
DOI: 10.3390/jpm13030547
Abstract: Purpose Recent integration of open-source data to machine learning models, especially in the medical field, has opened new doors to study disease progression and/or regression. However, the limitation of using medical data for machine learning… read more here.
Keywords: quality; synthetic images; prostate; adversarial networks ... See more keywords