Representation learning for time series has been an important research a...
We conducted a prospective study to measure the clinical impact of an
ex...
The work discusses the use of machine learning algorithms for anomaly
de...
Federated learning (FL) is a machine learning (ML) approach that allows ...
We introduce FedDCT, a novel distributed learning paradigm that enables ...
This paper addresses the few-shot image classification problem. One nota...
Classifying pill categories from real-world images is crucial for variou...
We propose a data collecting and annotation pipeline that extracts
infor...
Nowadays, an increasing number of people are being diagnosed with
cardio...
The COVID-19 pandemic has exposed the vulnerability of healthcare servic...
Human action recognition is an important application domain in computer
...
Recent artificial intelligence (AI) algorithms have achieved
radiologist...
The rapid development in representation learning techniques and the
avai...
Cardiovascular diseases (CVDs) are a group of heart and blood vessel
dis...
A fully automated system for interpreting abdominal computed tomography ...
Mammography, or breast X-ray, is the most widely used imaging modality t...
Computer-aided diagnosis systems in adult chest radiography (CXR) have
r...
Building an accurate computer-aided diagnosis system based on data-drive...
Image augmentation techniques have been widely investigated to improve t...
Advanced deep learning (DL) algorithms may predict the patient's risk of...
X-ray imaging in DICOM format is the most commonly used imaging modality...
Chest radiograph (CXR) interpretation in pediatric patients is error-pro...
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic...
Radiographs are used as the most important imaging tool for identifying ...
The chest X-rays (CXRs) is one of the views most commonly ordered by
rad...
Chest radiography is one of the most common types of diagnostic radiolog...