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Found 10 papers, 5 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.
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.
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
We find that the most important ingredients for high quality saliency map generation are (1) using both masked-in and masked-out objectives and (2) training the classifier alongside the masking model.
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.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
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.
Improving the Ability of Deep Neural Networks to Use Information From Multiple Views in Breast Cancer Screening
In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles.
Screening Mammogram Classification with Prior Exams
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Moreover, both the global structure and local details play important roles in medical image analysis tasks.
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).
