Linda Moy

According to our database1, Linda Moy authored at least 20 papers between 2004 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 




Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification.
CoRR, 2022

Lessons from the first DBTex Challenge.
Nat. Mach. Intell., 2021

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.
Medical Image Anal., 2021

Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms.
J. Digit. Imaging, 2021

A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction.
Proceedings of the Machine Learning for Medical Image Reconstruction, 2021

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.
IEEE Trans. Medical Imaging, 2020

Differences between human and machine perception in medical diagnosis.
CoRR, 2020

Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms.
CoRR, 2020

Understanding the robustness of deep neural network classifiers for breast cancer screening.
CoRR, 2020

Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Improving localization-based approaches for breast cancer screening exam classification.
CoRR, 2019

Screening Mammogram Classification with Prior Exams.
CoRR, 2019

Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.
Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

Breast Density Classification with Deep Convolutional Neural Networks.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks.
CoRR, 2017

Modeling annotator expertise: Learning when everybody knows a bit of something.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning From Crowds.
J. Mach. Learn. Res., 2010

Supervised learning from multiple experts: whom to trust when everyone lies a bit.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Clinical Application of a Semiautomatic 3D Fusion Tool Where Automatic Fusion Techniques Are Difficult to Use.
Proceedings of the Biomedical Image Registration, Third International Workshop, 2006

Can the Specificity of MRI Breast Imaging be Improved by Fusing 3D MRI Volume Data Sets with FDG PET?
Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2004