Search Results for author: Linda Moy

Found 14 papers, 6 papers with code

An efficient deep neural network to find small objects in large 3D images

1 code implementation16 Oct 2022 Jungkyu Park, Jakub Chłędowski, Stanisław Jastrzębski, 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

On a dataset collected at NYU Langone Health, including 85, 526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0. 831 (95% CI: 0. 769-0. 887) in classifying breasts with malignant findings using 3D mammography.

Anatomy

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

no code implementations3 Feb 2022 Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer

The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.

Image Classification

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

no code implementations19 Sep 2020 Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, S. Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras

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.

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

no code implementations23 Mar 2020 Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski, Tomasz Trzciński, Linda Moy, Kyunghyun Cho, Laura Heacock, Krzysztof J. Geras

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.

Improving localization-based approaches for breast cancer screening exam classification

no code implementations1 Aug 2019 Thibault Févry, Jason Phang, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

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).

Classification General Classification

Screening Mammogram Classification with Prior Exams

no code implementations30 Jul 2019 Jungkyu Park, Jason Phang, Yiqiu Shen, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras

Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.

Classification General Classification

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