Recent years have experienced phenomenal growth in computer-aided diagnosis systems
based on machine learning algorithms for anomaly detection tasks in the medical image …

Deep learning, in recent times, has made remarkable strides when it comes to impressive
performance for many tasks, including medical image processing. One of the contributing …

Abstract Background and Objective: Convolutional neural networks are widely used to
detect radiological findings in chest radiographs. Standard architectures are optimized for …

Aim Aortic elongation can result from age-related changes, congenital factors, aneurysms, or
conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and …

The purpose of the study was to assess the performance of readers in diagnosing thoracic
anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based …

We conducted a prospective study to measure the clinical impact of an explainable machine
learning system on interobserver agreement in chest radiograph interpretation. The AI …

The development of explanatory intelligence in connection to intelligent diagnostic systems
is critical in medical science. The paper provides ways for application of methods and …

Background Chest X-rays (CXRs) are widely used for cost-effective screening of active
pulmonary tuberculosis despite their limitations in sensitivity and specificity when interpreted …

The work discusses the use of machine learning algorithms for anomaly detection in medical
image analysis and how the performance of these algorithms depends on the number of …

Background: Ventilator associated pneumonia (VAP) is defined by the American College of
Surgeons Trauma Quality Improvement Program (ACS TQIP) using laboratory findings …