Generative adversarial networks in medical image augmentation: a review
Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …
image-based diagnosis and treatment models is increasing. Generative Adversarial …
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 …
[BOOK][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Denoising diffusion probabilistic models for 3D medical image generation
Recent advances in computer vision have shown promising results in image generation.
Diffusion probabilistic models have generated realistic images from textual input, as …
Diffusion probabilistic models have generated realistic images from textual input, as …
MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
Label-free liver tumor segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …
A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Y Gu, J Chi, J Liu, L Yang, B Zhang, D Yu… - Computers in biology …, 2021 - Elsevier
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Image synthesis with adversarial networks: A comprehensive survey and case studies
Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …
various application domains such as computer vision, medicine, and natural language …
Perceptual enhancement for autonomous vehicles: Restoring visually degraded images for context prediction via adversarial training
Realizing autonomous vehicles is one of the ultimate dreams for humans. However,
perceptual information collected by sensors in dynamic and complicated environments, in …
perceptual information collected by sensors in dynamic and complicated environments, in …
The Role of generative adversarial network in medical image analysis: An in-depth survey
M AlAmir, M AlGhamdi - ACM Computing Surveys, 2022 - dl.acm.org
A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …
the field of artificial intelligence, and its superior data generation capability has garnered …