The goal of this competition is to identify breast cancer. You’ll train your model with screening mammograms obtained from regular screening.
Your work improving the automation of detection in screening mammography may enable radiologists to be more accurate and efficient, improving the quality and safety of patient care. It could also help reduce costs and unnecessary medical procedures.
According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms.
Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. A looming shortage of radiologists in several countries will likely worsen this problem. Mammography screening also leads to a high incidence of false positive results. This can result in unnecessary anxiety, inconvenient follow-up care, extra imaging tests, and sometimes a need for tissue sampling (often a needle biopsy).
The competition host, the Radiological Society of North America (RSNA) is a non-profit organization that represents 31 radiologic subspecialties from 145 countries around the world. RSNA promotes excellence in patient care and health care delivery through education, research, and technological innovation.
Your efforts in this competition could help extend the benefits of early detection to a broader population. Greater access could further reduce breast cancer mortality worldwide.
Awards:-
1st Place – $ 10,000
2nd Place – $ 8,000
3rd Place – $ 7,000
4th – 8th Place(s) – $ 5,000
Because this competition is being hosted in coordination with the Radiological Society of North America (RSNA®) Annual Meeting, winners will be invited and strongly encouraged to attend the conference with waived fees, contingent on review of solution and fulfillment of winners’ obligations.
Note that, per the competition rules, in addition to the standard Kaggle Winners’ Obligations (open-source licensing requirements, solution packaging/delivery, presentation to host), the host team also asks that you:
(i) create a short video presenting your approach and solution, and
(ii) publish a link to your open sourced code on the competition forum
(iii) (strongly suggested) make some version of your model publicly available for more hands-on testing purposes only. As an example of a hosted algorithm, please see http://demos.md.ai/#/bone-age.\
Deadline:- 20-02-2023
