RSNA Pulmonary Embolism Detection Challenge (2020)
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. For the first time in an RSNA data challenge, the rules required competitors to submit and run their code in a standard shared environment, producing simpler, more readily usable models.
AI is a critical tool for radiologists in PE detection
PE is among the most fatal cardiovascular diseases, causing 60,000 to 100,000 deaths annually in the United States. Patients exhibit symptoms that are common to other diseases and rapid radiologic diagnosis is often critical to care decisions. This challenge demonstrates how machine learning can aid in more effective patient management and treatment by allowing radiologists to more accurately identify PE cases.
Of the 784 teams from around the world who took part in the challenge, 10 teams with the best scoring submissions will be recognized in a presentation during RSNA 2020. In recognition of the competition’s public value, the winning teams will share a total of $30,000 in prize money, provided by Kaggle.
Dataset
Review the PE Detection Challenge dataset description.
RSNA PE Detection Challenge: Winning entries
| Team name | Video | Solution |
|---|---|---|
| Guanshuo Xu | Video | Solution |
| HIGH D-DIMER | Video | Solution |
| VinBigData-Medical Imaging | Video | Solution |
| kazumax | Video | Solution |
| deepread.ai | Video | Solution |
| OsciiArt | Video | Solution |
| yuval reina | Video | Solution |
| [Aillis] Yuji + Jan + yama | Video | Solution |
| shimacha | Video | Solution |
| OrKatz | Video | Solution |
|
Team name: Guanshuo Xu Video: Video Solution: Solution |
|
Team name: HIGH D-DIMER Video: Video Solution: Solution |
|
Team name: VinBigData-Medical Imaging Video: Video Solution: Solution |
|
Team name: kazumax Video: Video Solution: Solution |
|
Team name: deepread.ai Video: Video Solution: Solution |
|
Team name: OsciiArt Video: Video Solution: Solution |
|
Team name: yuval reina Video: Video Solution: Solution |
|
Team name: [Aillis] Yuji + Jan + yama Video: Video Solution: Solution |
|
Team name: shimacha Video: Video Solution: Solution |
|
Team name: OrKatz Video: Video Solution: Solution |
Results
Access the PE Detection Challenge results on the Kaggle website.
2020 Educational Merit Award
The Educational Merit Award, newly created for 2020, is a distinction to recognize a winner from among the top 10 teams whose entry is deemed outstanding in the clarity, completeness, organization and efficiency of its submitted code.
The 2020 Educational Merit Award was presented to:
Ian Pan, MD
(Team name: HIGH D-DIMER)
Acknowledgments
Pulmonary Embolism Detection Challenge Acknowledgments