Recent Projects
RSNA STR Pulmonary Embolism Detection
Classify Pulmonary Embolism cases in chest CT scans
The 2020 RSNA Pulmonary Embolism Detection Challenge invited researchers to develop machine-learning algorithms to detect and characterize instances of pulmonary embolism (PE) on chest CT studies. The competition, conducted in collaboration with the Society of Thoracic Radiology (STR), involved creating the largest publicly available annotated PE dataset, comprised of more than 12,000 CT studies. Imaging data was contributed by five international research centers and labeled with detailed clinical annotations by a group of more than 80 expert thoracic radiologists.
RSNA Intracranial Hemorrhage Detection
Identify acute intracranial hemorrhage and its subtypes
The dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Radiological Society of North America (RSNA) and the American Society of Neuroradiology (ASNR), with data contributions from Stanford University, St. Michael's Hospital, Thomas Jefferson University, and Universidade Federal de São Paulo.
SIIM-ACR Pneumothorax Segmentation
Identify Pneumothorax disease in chest x-rays
The dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Society for Imaging Informatics in Medicine (SIIM) and the Society of Thoracic Radiology (STR).
RSNA Pneumonia Detection Challenge
Can you build an algorithm that automatically detects potential pneumonia cases? Completed in 2018.
The dataset for this Kaggle challenge was created on the MD.ai platform in collaboration with the Radiological Society of North America (RSNA) and the Society of Thoracic Radiology (STR).
White Papers
RSNA Pneumonia Project - How to Tame Data in the Digital Age
Description of the pneumonia project and adjudication process.
Crowdsourcing Annotations
Description of crowdsourced annotations for the RSNA Pneumonia Challenge.
Google Cloud Platform Case Study
MD.ai leverages Google Cloud Platform and Cloud Healthcare API to create annotated datasets and build algorithms for machine learning to bring better insights to medical providers.