34 citations · 55 across the 5 of their papers we have counts for
5 papers
Causality for Inherently Explainable Transformers: CAT-XPLAIN
Subash Khanal, Benjamin Brodie, Xin Xing +2
There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks. However, there is still a gap in research efforts toward designi…
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…
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…