A deep learning algorithm for reducing false positives in screening mammography

no code implementations • 13 Apr 2022 • Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M. Appleton, Jason Su, Richard L. Wahl

This work lays the foundation for semi-autonomous breast cancer screening systems that could benefit patients and healthcare systems by reducing false positives, unnecessary procedures, patient anxiety, and expenses.

A Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images

no code implementations • 23 Jan 2020 • Thomas P. Matthews, Sadanand Singh, Brent Mombourquette, Jason Su, Meet P. Shah, Stefano Pedemonte, Aaron Long, David Maffit, Jenny Gurney, Rodrigo Morales Hoil, Nikita Ghare, Douglas Smith, Stephen M. Moore, Susan C. Marks, Richard L. Wahl

Conclusion: A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved using few SM images.