Breast cancer screening is an important aspect of healthcare for women aged 40-75. While it is an invaluable tool, the prevalence of cancer in this age group is relatively low, with only about five out of every 1,000 women being diagnosed. However, one of the challenges of breast cancer screening lies in the occurrence of false positives, which can lead to anxiety and unnecessary medical procedures for patients. The key to improving the screening process is addressing these false positives, and researchers Nhi and Dan are turning to computer-aided detection (CAD) software to assist radiologists in this mission. “When a woman goes into screening, they will take four images, two images of each breast, and usually only one breast will be cancerous,” Nhi explains. “If CAD software produced one false positive per image, radiologists must dismiss 400 false positive marks for every meaningful cancer detected. That’s a huge workload. It’s a lot of resources wasted. Women have to go through a lot of extra procedures, anxiety, and in some cases, even biopsy, which is a very serious procedure.” The primary objective is to develop high-sensitivity CAD software while maintaining a low false positive rate. Achieving this goal is far from straightforward, as medical imaging 12 DAILY MICCAI Tuesday Poster Presentation M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector Yen Nhi Truong Vu (left) and Dan Guo (right) are Research Scientists at Whiterabbit.ai working under the Director of Research, Thomas Paul Matthews. Their paper proposes a system to reduce the number of false positives for breast cancer screening. They speak to us ahead of their poster this afternoon.
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