Search Results for author:
Found 9 papers, 6 papers with code
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
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
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.
An efficient deep neural network to find small objects in large 3D images
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.
Differences between human and machine perception in medical diagnosis
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
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
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
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
