Linda Moy

According to our database, Linda Moy authored at least 25 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.

Jungkyu Park, Jakub Chledowski, Stanislaw Jastrzebski, Jan Witowski, Yanqi Xu, Linda Du, Sushma Gaddam, Eric Kim, Alana Lewin, Ujas Parikh, Anastasia Plaunova, Sardius Chen, Alexandra Millet, James Park, Kristine Pysarenko, Shalin Patel, Julia Goldberg, Melanie Wegener, Linda Moy, Laura Heacock, Beatriu Reig, Krzysztof J. Geras

IEEE Trans. Medical Imaging, January, 2024

fastMRI Breast: A publicly available radial k-space dataset of breast dynamic contrast-enhanced MRI.

Eddy Solomon, Patricia M. Johnson, Zhengguo Tan, Radhika Tibrewala, Yvonne W. Lui, Florian Knoll, Linda Moy, Sungheon Gene Kim, Laura Heacock

CoRR, 2024

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.

Farhad Maleki, Linda Moy, Reza Forghani, Tapotosh Ghosh, Katie L. Ovens, Steve Langer, Pouria Rouzrokh, Bardia Khosravi, Ali Ganjizadeh, Daniel I. Warren, Roxana Daneshjou, Mana Moassefi, Atlas Haddadi Avval, Susan Sotardi, Neil A. Tenenholtz, Felipe Kitamura, Timothy Kline

CoRR, 2024

2023

Updating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for reporting AI research.

Ali S. Tejani, Michail E. Klontzas, Anthony A. Gatti, John Mongan, Linda Moy, Seong Ho Park, Charles E. Kahn

Nat. Mac. Intell., September, 2023

2022

3D-GMIC: an efficient deep neural network to find small objects in large 3D images.

Jungkyu Park, Jakub Chledowski, Stanislaw Jastrzebski, Jan Witowski, Yanqi Xu, Linda Du, Sushma Gaddam, Eric Kim, Alana Lewin, Ujas Parikh, Anastasia Plaunova, Sardius Chen, Alexandra Millet, James Park, Kristine Pysarenko, Shalin Patel, Julia Goldberg, Melanie Wegener, Linda Moy, Laura Heacock, Beatriu Reig, Krzysztof J. Geras

CoRR, 2022

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

Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil A. Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer

CoRR, 2022

2021

Lessons from the first DBTex Challenge.

Jungkyu Park, Yoel Shoshan, Robert Martí, Pablo Gómez del Campo, Vadim Ratner, Daniel Khapun, Aviad Zlotnick, Ella Barkan, Flora Gilboa-Solomon, Jakub Chledowski, Jan Witowski, Alexandra Millet, Eric Kim, Alana Lewin, Kristine Pysarenko, Sardius Chen, Julia Goldberg, Shalin Patel, Anastasia Plaunova, Melanie Wegener, Stacey Wolfson, Jiyon Lee, Sana Hava, Sindhoora Murthy, Linda Du, Sushma Gaddam, Ujas Parikh, Laura Heacock, Linda Moy, Beatriu Reig, Michal Rosen-Zvi, Krzysztof J. Geras

Nat. Mach. Intell., 2021

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Kangning Liu, Sudarshini Tyagi, Laura Heacock, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Medical Image Anal., 2021

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

Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, Sungheon Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras

J. Digit. Imaging, 2021

A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction.

Zhengnan Huang, Jonghyun Bae, Patricia M. Johnson, Terlika Sood, Laura Heacock, Justin Fogarty, Linda Moy, Sungheon Gene Kim, Florian Knoll

Proceedings of the Machine Learning for Medical Image Reconstruction, 2021

2020

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, Stanislaw Jastrzebski, Thibault Févry, Joe Katsnelson, Eric Kim, Stacey Wolfson, Ujas Parikh, Sushma Gaddam, Leng Leng Young Lin, Kara Ho, Joshua D. Weinstein, Beatriu Reig, Yiming Gao, Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, Krystal Airola, Eralda Mema, Stephanie Chung, Esther Hwang, Naziya Samreen, Sungheon Gene Kim, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

IEEE Trans. Medical Imaging, 2020

Differences between human and machine perception in medical diagnosis.

Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz, Celin Chacko, Robin Ehrenpreis, Naziya Samreen, Chloe Chhor, Eric Kim, Jiyon Lee, Kristine Pysarenko, Beatriu Reig, Hildegard Toth, Divya Awal, Linda Du, Alice Kim, James Park, Daniel K. Sodickson, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

CoRR, 2020

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

Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, Sungheon Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras

CoRR, 2020

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

Witold Oleszkiewicz, Taro Makino, Stanislaw Jastrzebski, Tomasz Trzcinski, Linda Moy, Kyunghyun Cho, Laura Heacock, Krzysztof J. Geras

CoRR, 2020

Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening.

Nan Wu, Stanislaw Jastrzebski, Jungkyu Park, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

2019

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

Thibault Févry, Jason Phang, Nan Wu, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

CoRR, 2019

Screening Mammogram Classification with Prior Exams.

Jungkyu Park, Jason Phang, Yiqiu Shen, Nan Wu, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

CoRR, 2019

Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.

Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

2018

Breast Density Classification with Deep Convolutional Neural Networks.

Nan Wu, Krzysztof J. Geras, Yiqiu Shen, Jingyi Su, Sungheon Gene Kim, Eric Kim, Stacey Wolfson, Linda Moy, Kyunghyun Cho

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

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

Krzysztof J. Geras, Stacey Wolfson, Sungheon Gene Kim, Linda Moy, Kyunghyun Cho

CoRR, 2017

2010

Modeling annotator expertise: Learning when everybody knows a bit of something.

Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning From Crowds.

Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy

J. Mach. Learn. Res., 2010

2009

Supervised learning from multiple experts: whom to trust when everyone lies a bit.

Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2006

Clinical Application of a Semiautomatic 3D Fusion Tool Where Automatic Fusion Techniques Are Difficult to Use.

Marilyn E. Noz, Gerald Q. Maguire Jr., Michael P. Zeleznik, Lotta Olivecrona, Henrik Olivecrona, Leon Axel, Mondavi B. Srichai, Linda Moy, Antoinette Murphy-Walcott

Proceedings of the Biomedical Image Registration, Third International Workshop, 2006

2004

Can the Specificity of MRI Breast Imaging be Improved by Fusing 3D MRI Volume Data Sets with FDG PET?

Marilyn E. Noz, Linda Moy, Fabio Ponzo, Elissa L. Kramer, Gerald Q. Maguire Jr.

Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2004