Deep learning has shown recent success in classifying anomalies in chest x-rays, but
datasets are still small compared to natural image datasets. Supervision of abnormality …

In the field of chest X-ray (CXR) diagnosis, existing works often focus solely on determining
where a radiologist looks, typically through tasks such as detection, segmentation, or …

Deep learning technologies have been widely adopted in medical imaging due to their
ability to extract features from images and make accurate diagnoses automatically. Medical …

The interpretability of medical image analysis models is considered a key research field. We
use a dataset of eye-tracking data from five radiologists to compare the outputs of …

Human-computer interaction research has to play a vital role in increasing the adoption of
deep learning models in clinical settings, as their adoption is low despite models …

In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract
labels from reports, but concerns exist about label quality. These datasets typically offer only …

Deep learning has been widely applied to chest x-ray analysis. Visual representations of
data can help explain the decisions of a deep learning model and boost learning when …