Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …

In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for various imaging related tasks such as artificial image generation to support AI training …

Despite the recent success in applying supervised deep learning to medical imaging tasks,
the problem of obtaining large and diverse expert-annotated datasets required for the …

Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …

Skin cancer is one of the primary causes of death in the world. Timely diagnosis of skin
cancer can reduce the number of deaths. Skin cancer can be diagnosed early using deep …

For optimum performance, deep learning methods, such as those applied for retinal and
choroidal layer segmentation in optical coherence tomography (OCT) images, require …

In view of the difficulty in training the algorithm of image oil painting style migration and
reconstruction based on the generative adversarial network, and the loss gradient of …

Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include high …

Data augmentation is an important strategy for enlarging training datasets in deep learning-
based medical image analysis. This is because large, annotated medical datasets are not …

Air pollution is one of the major health hazards in modern times. Vehicle pollution is one of
the major contributors to aerial contamination. Significant emphasis has been given by the …