Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

Limited availability of medical imaging datasets is a vital limitation when using “data hungry”
deep learning to gain performance improvements. Dealing with the issue, transfer learning …

Deep learning methods have achieved great success in medical image analysis domain.
However, most of them suffer from slow convergency and high computing cost, which …

Featured Application We aim to present an automatic tool to classify between chest diseases
such as pneumonia and healthy patients to assist a medical diagnosis even when there are …

Around the world, several lung diseases such as pneumonia, cardiomegaly, and
tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for …

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in
assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and …

The development of deep learning technology has enabled machines to achieve high-level
accuracy in interpreting medical images. While many previous studies have examined the …

As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus,
which has become the pandemic COVID-19. This aggressive disease deteriorates the …

We examined the feasibility of explainable computer-aided detection of cardiomegaly in
routine clinical practice using segmentation-based methods. Overall, 793 retrospectively …