Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Pre-training lays the foundation for recent successes in radiograph analysis supported by
deep learning. It learns transferable image representations by conducting large-scale fully …

Data augmentation has been actively studied for robust neural networks. Most of the recent
data augmentation methods focus on augmenting datasets during the training phase. At the …

Purpose To evaluate code and data sharing practices in original artificial intelligence (AI)
scientific manuscripts published in the Radiological Society of North America (RSNA) …

Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early
and correct diagnosis is vital to avoid health complexities, however, it depends upon the …

Interpretation of chest radiographs (CXR) is a difficult but essential task for detecting thoracic
abnormalities. Recent artificial intelligence (AI) algorithms have achieved radiologist-level …

Image-based teleconsultation using smartphones has become increasingly popular. In
parallel, deep learning algorithms have been developed to detect radiological findings in …

Automatic extraction of medical conditions from free-text radiology reports is critical for
supervising computer vision models to interpret medical images. In this work, we show that …

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs).
Many interpretation methods help to understand the decision-making of DNNs by generating …