Analyzing medical data to find abnormalities is a time-consuming and costly task,
particularly for rare abnormalities, requiring tremendous efforts from medical experts …

Modern deep learning methods achieve state-ofthe-art results in many computer vision
tasks. While these methods perform well when trained on large datasets, deep learning …

A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …

Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

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 …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Abstract Inspired by classic Generative Adversarial Networks (GANs), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical image …

Semantic segmentation is a fundamental problem in biomedical image analysis. In
biomedical practice, it is often the case that only limited annotated data are available for …

Segmentation of regions of interest (ROIs) in medical images is an important step for image
analysis in computer-aided diagnosis systems. In recent years, segmentation methods …

Medical imaging has become integral to modern healthcare, enabling non-invasive
visualization and assessment of anatomical structures. However, medical imaging datasets …