23 citations · 27 across the 8 of their papers we have counts for
8 papers
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Yu Zhang, Gongbo Liang, Nathan Jacobs +1
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on pub…
Joint 2D-3D Breast Cancer Classification
Gongbo Liang, Xiaoqin Wang, Yu Zhang +4
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D…
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Yu Zhang, Xiaoqin Wang, Hunter Blanton +3
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical pr…
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Yu Zhang, Gongbo Liang, Tawfiq Salem +1
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and d…
FARSA: Fully Automated Roadway Safety Assessment
Weilian Song, Scott Workman, Armin Hadzic +5
This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP)…
Predicting Ground-Level Scene Layout from Aerial Imagery
Menghua Zhai, Zachary Bessinger, Scott Workman +1
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (…