Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

A You, JK Kim, IH Ryu, TK Yoo - Eye and Vision, 2022 - Springer
Background Recent advances in deep learning techniques have led to improved diagnostic
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …

Applications of artificial intelligence in dentistry: A comprehensive review

F Carrillo‐Perez, OE Pecho, JC Morales… - Journal of Esthetic …, 2022 - Wiley Online Library
Objective To perform a comprehensive review of the use of artificial intelligence (AI) and
machine learning (ML) in dentistry, providing the community with a broad insight on the …

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction

H Shan, A Padole, F Homayounieh, U Kruger… - Nature Machine …, 2019 - nature.com
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …

Medical image super-resolution reconstruction algorithms based on deep learning: A survey

D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …

Eformer: Edge enhancement based transformer for medical image denoising

A Luthra, H Sulakhe, T Mittal, A Iyer… - ar** review
LT Arsiwala-Scheppach, A Chaurasia… - Journal of Clinical …, 2023 - mdpi.com
Machine learning (ML) is being increasingly employed in dental research and application.
We aimed to systematically compile studies using ML in dentistry and assess their …

Progress in deep learning-based dental and maxillofacial image analysis: A systematic review

NK Singh, K Raza - Expert Systems with Applications, 2022 - Elsevier
Background With the advent of deep learning in modern computing there has been
unprecedented progress in image processing and segmentation. Deep learning-based …

A review of deep learning methods for denoising of medical low-dose CT images

J Zhang, W Gong, L Ye, F Wang, Z Shangguan… - Computers in Biology …, 2024 - Elsevier
To prevent patients from being exposed to excess of radiation in CT imaging, the most
common solution is to decrease the radiation dose by reducing the X-ray, and thus the …

Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising

T Liang, Y **, Y Li, T Wang - 2020 15th IEEE International …, 2020 - ieeexplore.ieee.org
In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose
CT image denoising has attracted extensive attention from researchers, which has become …