Deep Learning for Pulmonary Embolism Detection: Tackling the RSNA 2020 AI Challenge

In 2020, the Radiological Society of North America and Society of Thoracic Radiology sponsored a machine learning competition to detect and classify pulmonary embolism (PE). This challenge was one of the largest of its kind, with more than 9000 CT pulmonary angiography examinations comprising almost 1.8 million expertly annotated images. More than 700 international teams competed to predict the presence of PE on individual axial images, the overall presence of PE in the CT examination (with chronicity and laterality), and the ratio of right ventricular size to left ventricular size. This article presents a detailed overview of the second-place solution. Source code and models are available at https://github.com/i-pan/kaggle-rsna-pe. Keywords: CT, Neural Networks, Thorax, Pulmonary Arteries, Embolism/Thrombosis, Supervised Learning, Convolutional Neural Networks (CNN), Machine Learning Algorithms © RSNA, 2021