Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Deep learning has been widely used in medical image segmentation and other aspects.
However, the performance of existing medical image segmentation models has been limited …

Consistency learning is a central strategy to tackle unlabeled data in semi-supervised
medical image segmentation (SSMIS) which enforces the model to produce consistent …

The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate
pneumonia infection segmentation is important for assisting doctors in diagnosing COVID …

Background and objective: Deep learning-based methods for fast target segmentation of
magnetic resonance imaging (MRI) have become increasingly popular in recent years …

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …

Over the past few years, the rapid development of deep learning technologies for computer
vision has significantly improved the performance of medical image segmentation …

This article presents a systematic overview of artificial intelligence (AI) and computer vision
strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized …