According to the WHO, breast cancer is the most common 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 at-risk age groups.
Mammography equipment. Image credit: AlarconAudiovisual via Pixabay, free license
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 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.
Submissions to this Challenge must be received by 11:59 PM UTC, February 27, 2023.
Source: Kaggle