A Hypersensitive Breast Cancer Detector

no code implementations • 23 Jan 2020 • Stefano Pedemonte, Brent Mombourquette, Alexis Goh, Trevor Tsue, Aaron Long, Sadanand Singh, Thomas Paul Matthews, Meet Shah, Jason Su

In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity.

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.

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

no code implementations • 23 Jan 2020 • Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su

In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images.

Specificity