17 citations · 50 across the 12 of their papers we have counts for
5 papers · 1 filter
Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
Ruiyu Jia, Yanqi Xu, Yuxuan Chen +2
Mammogram-based deep learning models have improved breast cancer risk prediction, but the learned imaging patterns remain underexplored. Existing interpretability methods rely on s…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Jungkyu Park, Jan Witowski, Yanqi Xu +8
Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Yiqiu Shen, Jungkyu Park, Frank Yeung +4
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models a…
Differences between human and machine perception in medical diagnosis
Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz +18
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which h…
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…