Search Results for author: Laura Heacock

Found 11 papers, 6 papers with code

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

no code implementations7 Jun 2024 Eddy Solomon, Patricia M. Johnson, Zhengguo Tan, Radhika Tibrewala, Yvonne W. Lui, Florian Knoll, Linda Moy, Sungheon Gene Kim, Laura Heacock

The public availability of this dataset and accompanying reconstruction code will support research and development of fast and quantitative breast image reconstruction and machine learning methods.

Image Reconstruction

Understanding differences in applying DETR to natural and medical images

no code implementations27 May 2024 Yanqi Xu, Yiqiu Shen, Carlos Fernandez-Granda, Laura Heacock, Krzysztof J. Geras

This study evaluates the applicability of these transformer-based design choices when applied to a screening mammography dataset that represents these distinct medical imaging data characteristics.

Medical Diagnosis object-detection +1

Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data

no code implementations6 Nov 2023 Yiqiu Shen, Jungkyu Park, Frank Yeung, Eliana Goldberg, Laura Heacock, Farah Shamout, Krzysztof J. Geras

Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue.

BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors

1 code implementation17 Apr 2023 Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang, Oishi Banerjee, Farah Dadabhoy, Veeral Vipin Mehta, Ryan Wonhee Han, Fang Cao, Raja R. Narayan, Errol Colak, Adewole Adamson, Laura Heacock, Geoffrey H. Tison, Alex Tamkin, Pranav Rajpurkar

Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.

Self-Supervised Learning

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

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.

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