195 citations · 298 across the 12 of their papers we have counts for
12 papers
An efficient deep neural network to find small objects in large 3D images
Jungkyu Park, Jakub Chłędowski, Stanisław Jastrzębski +19
3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they…
Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification
Timothy L. Kline, Felipe Kitamura, Ian Pan +12
With the recent advances in A.I. methodologies and their application to medical imaging, there has been an explosion of related research programs utilizing these techniques to prod…
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…
Understanding the robustness of deep neural network classifiers for breast cancer screening
Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski +5
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented…
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang +8
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work…