When you have a broken arm, radiologists help save the day—and the bone. These doctors diagnose and treat medical conditions using imaging techniques like CT and PET scans, MRIs, and, of course, X-rays. Yet, as it happens when working with such a wide variety of medical tools, radiologists face many daily challenges, perhaps the most difficult being the chest radiograph. The interpretation of chest X-rays can lead to medical misdiagnosis, even for the best practicing doctor. Computer-aided detection and diagnosis systems (CADe/CADx) would help reduce the pressure on doctors at metropolitan hospitals and improve diagnostic quality in rural areas.
Existing methods of interpreting chest X-ray images classify them into a list of findings. There is currently no specification of their locations on the image which sometimes leads to inexplicable results. A solution for localizing findings on chest X-ray images is needed for providing doctors with more meaningful diagnostic assistance.
Established in August 2018 and funded by the Vingroup JSC, the Vingroup Big Data Institute (VinBigData) aims to promote fundamental research and investigate novel and highly-applicable technologies. The Institute focuses on key fields of data science and artificial intelligence: computational biomedicine, natural language processing, computer vision, and medical image processing. The medical imaging team at VinBigData conducts research in collecting, processing, analyzing, and understanding medical data. They’re working to build large-scale and high-precision medical imaging solutions based on the latest advancements in artificial intelligence to facilitate effective clinical workflows.
In this competition, you’ll automatically localize and classify 14 types of thoracic abnormalities from chest radiographs. You’ll work with a dataset consisting of 18,000 scans that have been annotated by experienced radiologists. You can train your model with 15,000 independently-labeled images and will be evaluated on a test set of 3,000 images. These annotations were collected via VinBigData’s web-based platform, VinLab. Details on building the dataset can be found in our recent paper “VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations”.
If successful, you’ll help build what could be a valuable second opinion for radiologists. An automated system that could accurately identify and localize findings on chest radiographs would relieve the stress of busy doctors while also providing patients with a more accurate diagnosis.
- 1st Place – $20,000
- 2nd Place – $14,000
- 3rd Place – $8,000
Participants are strictly prohibited against hand-labeling the test set, including use of any hand-labeling of the test set to inform model training or selection. The host will be thoroughly reviewing winning teams’ full source code. Team(s) found violating this rule will be disqualified and removed from the competition.
Special Prize: $8,000
- Awarded to the highest performing team (based on private leaderboard score) whose team members are all Vietnamese citizens.
- Team members need not currently be Vietnam residents, but must be current valid passport-holders to qualify.
- A team may only win either the Leaderboard Prize or the Special Prize, but not both. If a Leaderboard Prize winner is also a Vietnamese team, we will move down the leaderboard to identify the next Vietnamese team who would qualify for the Special Prize.
- We will open up a form towards the end of the competition for eligible teams to submit for consideration.