Background Chest radiograph interpretation is critical for the detection of thoracic diseases,
including tuberculosis and lung cancer, which affect millions of people worldwide each year …

Automated radiological screening is an advancing field in which algorithms and predictive
models are used to detect abnormalities in Chest X-rays (CXRs). Traditionally, in machine …

The well-documented global shortage of radiologists is most acutely manifested in countries
where the rapid rise of a middle class has created a new capacity to produce imaging …

Chest X-ray is the most common medical imaging exam used to assess multiple
pathologies. Automated algorithms and tools have the potential to support the reading …

Convolutional neural networks have been applied widely in chest X-ray interpretation thanks
to the availability of high-quality datasets. Among them, VinDr-CXR is one of the latest public …

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However,
the current workload in extensive health care facilities and lack of well-trained radiologists is …

Chest X-ray scans are frequently requested to detect the presence of abnormalities, due to
their low-cost and non-invasive nature. The interpretation of these images can be automated …

Being one of the most common diagnostic imaging tests, chest radiography requires timely
reporting of potential findings in the images. In this paper, we propose an end-to-end …

Chest X-ray becomes one of the most common medical diagnoses due to its
noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X …

A major obstacle to the integration of deep learning models for chest x-ray interpretation into
clinical settings is the lack of understanding of their failure modes. In this work, we first …