34 citations · 123 across the 15 of their papers we have counts for
9 papers · 1 filter
Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
Gongbo Liang, Yuanyuan Su, Sheng-Chieh Lin +3
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding th…
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
Xin Xing, Gongbo Liang, Hunter Blanton +4
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally…
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
Gongbo Liang, Connor Greenwell, Yu Zhang +3
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, tex…
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Gongbo Liang, Yu Zhang, Xiaoqin Wang +1
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these wo…
A deep learning view of the census of galaxy clusters in IllustrisTNG
Y. Su, Y. Zhang, G. Liang +8
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X…
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…