Search Results for author: Brent Mombourquette

Found 6 papers, 1 papers with code

Problems and shortcuts in deep learning for screening mammography

no code implementations29 Mar 2023 Trevor Tsue, Brent Mombourquette, Ahmed Taha, Thomas Paul Matthews, Yen Nhi Truong Vu, Jason Su

The original model trained on both datasets achieved a 0. 945 AUC on the combined US+UK dataset but paradoxically only 0. 838 and 0. 892 on the US and UK datasets, respectively.

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Deep is a Luxury We Don't Have

1 code implementation11 Aug 2022 Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette, Thomas Paul Matthews, Jason Su, Sadanand Singh

In this paper, we tackle this complexity by leveraging a linear self-attention approximation.

A deep learning algorithm for reducing false positives in screening mammography

no code implementations13 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 Hypersensitive Breast Cancer Detector

no code implementations23 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.

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

no code implementations23 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.

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