70 citations · 130 across the 9 of their papers we have counts for
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eess.IV2020★ 2 cited
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Nan Wu, Zhe Huang, Yiqiu Shen +8
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate…
eess.IV2019
Improving localization-based approaches for breast cancer screening exam classification
Thibault Févry, Jason Phang, Nan Wu +4
We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200,000 exams (over 1,000,000 images). Our model achieves an AUC of 0…
eess.IV2019
Screening Mammogram Classification with Prior Exams
Jungkyu Park, Jason Phang, Yiqiu Shen +5
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new ne…